tag:blogger.com,1999:blog-9878509324340015592024-03-19T12:36:58.118+01:00The 20% StatisticianA blog on statistics, methods, philosophy of science, and open science. Understanding 20% of statistics will improve 80% of your inferences.Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.comBlogger122125tag:blogger.com,1999:blog-987850932434001559.post-14703651924997820632024-02-09T08:38:00.007+01:002024-02-12T13:37:03.613+01:00Why Effect Sizes Selected for Significance are Inflated<p><span lang="EN-US">Estimates
based on samples from the population will show variability. The larger the
sample, the closer our estimates will be to the true population values.
Sometimes we will observe larger estimates than the population value, and
sometimes we will observe smaller values. As long as we have an unbiased
collection of effect size estimates, combining effect sizes estimates through a
meta-analysis can increase the accuracy of the estimate. Regrettably, the
scientific literature is often biased. It is specifically common that
statistically significant studies are published (e.g., studies with <i>p</i> values
smaller than 0.05) while studies with <i>p</i> values larger than 0.05 remain
unpublished </span><span lang="EN-US">(Ensinck & Lakens, 2023; Franco et al., 2014;
Sterling, 1959)</span><span lang="EN-US">. Instead of having access to all
effect sizes, anyone reading the literature only has access to effects that
passed a <i>significance filter</i>. This will introduce systematic bias in our
effect size estimates.</span></p>
<p class="MsoNormal">The explain
how selection for significance introduces bias, it is useful to understand the
concept of a <i>truncated </i>or <i>censored</i> distribution. If we want to
measure the average length of people in The Netherlands we would collect a
representative sample of individuals, measure how tall they are, and compute
the average score. If we collect sufficient data the estimate will be close to
the true value in the population. However, if we collect data from participants
who are on a theme park ride where people need to be at least 150 centimeters
tall to enter, the mean we compute is
based on a truncated distribution where only individuals taller than 150 cm are
included. Smaller individuals are missing. Imagine we have measured the height
of two individuals in the theme park ride, and they are 164 and 184 cm tall. Their
average height is (164+184)/2 = 174 cm. Outside the entrance of the theme park
ride is one individual who is 144 cm tall. Had we measured this individual as
well, our estimate of the average length would be (144+164+184)/3 = 164 cm. Removing
low values from a distribution will lead to overestimation of the true value.
Removing high values would lead to underestimation of the true value.</p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The
scientific literature suffers from publication bias. Non-significant test
results – based on whether a <i>p</i> value is smaller than 0.05 or not – are often
less likely to be published. When an effect size estimate is 0 the <i>p</i>
value is 1. The further removed effect sizes are from 0, the smaller the <i>p</i>
value. All else equal (e.g., studies have the same sample size, and measures
have the same distribution and variability) if results are selected for
statistical significance (e.g., <i>p</i> < .05) they are also selected for
larger effect sizes. As small effect sizes will be observed with their
corresponding probabilities, their absence will inflate effect size estimates. Every
study in the scientific literature provides it’s own estimate of the true
effect size, just as every individual provides it’s own estimate of the average
height of people in a country. When these estimates are combined – as happens
in meta-analyses in the scientific literature – the meta-analytic effect size
estimate will be biased (or systematically different from the true
population value) whenever the distribution is truncated. To achieve unbiased
estimates of population values when combining individual studies in the
scientific literature in meta-analyses researchers need access to the complete
distribution of values – or all studies that are performed, regardless of
whether they yielded a <i>p</i> value above or below 0.05.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In the
figure below we see a distribution centered at an effect size of Cohen’s d =
0.5 for a two-sided <i>t</i>-test with 50 observations in each independent
condition. Given an alpha level of 0.05 in this test only effect sizes larger
than d = 0.4 will be statistically significant (i.e., all observed effect sizes
in the grey area). The threshold for which observed effect sizes will be
statistically significant is determined by the sample size and the alpha level
(and not influenced by the true effect size). The white <span style="mso-spacerun: yes;"> </span>area under the curve illustrates Type 2 errors
– non-significant results that will be observed if the alternative hypothesis
is true. If researchers only have access to the effect sizes estimates in the
grey area – a truncated distribution where non-significant results are removed –
a weighted average effect size from only these studies will be upwardly biased.
<o:p></o:p></span></p>
<p class="MsoNormal"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEhw750XZ8KnwPTMr8caQRMLCJbbck3qVCcHLulcoDoxO7fBj6HPb8vKE8SWkFMs2Q_AbjYa3UqBrRySOVoCJKL0VRRBHXJ8Xks0KBYQqgM30d7VuVWe5Z0DtbJ5ixCVcM_cvzCRSApybYiU8lM4RjY-VJK1rVW8CwyMmPexOsSo9ce3oo9W0u7-7xWbRgA" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="675" data-original-width="945" height="286" src="https://blogger.googleusercontent.com/img/a/AVvXsEhw750XZ8KnwPTMr8caQRMLCJbbck3qVCcHLulcoDoxO7fBj6HPb8vKE8SWkFMs2Q_AbjYa3UqBrRySOVoCJKL0VRRBHXJ8Xks0KBYQqgM30d7VuVWe5Z0DtbJ5ixCVcM_cvzCRSApybYiU8lM4RjY-VJK1rVW8CwyMmPexOsSo9ce3oo9W0u7-7xWbRgA=w400-h286" width="400" /></a></div><div><br /></div>If researchers only have access to the effect sizes estimates in the grey area – a truncated distribution where non-significant results are removed – a weighted average effect size from only these studies will be upwardly biased. We can see this in the two forest plots visualizing meta-analyses below. In the top meta-analysis all 5 studies are included, even though study C and D yield non-significant results (as can be seen from the fact that the 95% CI overlaps with 0). The estimated effect size based on all 5 studies is d = 0.4. In the bottom meta-analysis the two non-significant studies are removed - as would happen when there is publication bias. Without these two studies the estimated effect size in the meta-analysis, d = 0.5, is inflated. The extent to which meta-analyses are inflated depends on the true effect size and the sample size of the studies.<div><br /><div> <a href="https://blogger.googleusercontent.com/img/a/AVvXsEgCw5OkfReJiOWmxv_d0fqh6cNjHqJfZLsc-3Z1DuXK0JhciMyEFmIxYEweGAoGewn7dfTnV6hy_2TUavqcbCjMcCUZFyAIr9qMYlvT4fKbLMXDzulC9NeyCod96UwLxjSe2S_tBTh5YPaoXTKNPRbn2w4LYPxO_C7EbbM9ZS_PPDminN3AmFWjgld6R0k" style="margin-left: 1em; margin-right: 1em; text-align: center;"><img alt="" data-original-height="2500" data-original-width="2000" height="782" src="https://blogger.googleusercontent.com/img/a/AVvXsEgCw5OkfReJiOWmxv_d0fqh6cNjHqJfZLsc-3Z1DuXK0JhciMyEFmIxYEweGAoGewn7dfTnV6hy_2TUavqcbCjMcCUZFyAIr9qMYlvT4fKbLMXDzulC9NeyCod96UwLxjSe2S_tBTh5YPaoXTKNPRbn2w4LYPxO_C7EbbM9ZS_PPDminN3AmFWjgld6R0k=w626-h782" width="626" /></a><div><br /><br /><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The inflation
will be greater the larger the part of the distribution is truncated, and the
closer the true population effect size is to 0. In our example about the height
of individuals the inflation would be greater had we truncated the distribution
by removing everyone smaller than 170 cm instead of 150 cm. If the true average
height of individuals was 194 cm, removing the few people that are expected to
be smaller than 150 (based on the assumption of normally distributed data) would
have less of an effect on how much our estimate is inflated than when the true
average height was 150 cm, in which case we would remove 50% of individuals. In
statistical tests where results are selected for significance at a 5% alpha
level more data will be removed if the true effect size is smaller, but also
when the sample size is smaller. If the sample size is smaller, statistical
power is lower, and more of the values in the distribution (those closest to 0)
<span style="mso-spacerun: yes;"> </span>will be non-significant.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Any single
estimate of a population value will vary around the true population value. The
effect size estimate from a single study can be smaller than the true effect
size, even if studies have been selected for significance. For example, it is
possible that the true effect size is 0.5, you have observed an effect size of
0.45, but only effect sizes smaller than 0.4 are truncated when selecting
studies based on statistical significance (as in the figure above). At the same
time, this single effect size estimate of 0.45 is inflated. What inflates the
effect size is the long-run procedure used to generate the value. In the long
run effect sizes estimates based on a procedure where estimates are selected
for significance will be upwardly biased. This means that a single observed
effect size of d = 0.45 will be inflated if it is generated based on a
procedure where all non-significant effects are truncated, but it will be
unbiased if it is generated based on a distribution where all observed effect
sizes are reported, regardless of whether they are significant or not. This
also means that a single researcher can not guarantee that the effect sizes
they contribute to a literature will contribute to an unbiased effect sizes
estimate: There needs to be a system in place where all researchers report all
observed effect sizes to prevent bias. An alternative is to not have to rely on
other researchers, and collect sufficient data in a single study to have a
highly accurate effect size estimate. Multi-lab replication studies are an example
of such an approach, where dozens of researchers collect a large number (up to
thousands) of observations.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The most
extreme consequence of the inflation of effect size estimates occurs when the
true effect size in the population is 0, but due to selection of statistically
significant results, only significant effects in the expected direction are
published. Note that if all significant results are published (and not only
effect sizes in the expected direction) 2.5% of Type 1 error rates will be in
the positive direction, and 2.5% will be in the negative direction, and the
average effect size would be actually be 0. Thus, as long as the true effect
size is exactly 0, and all Type 1 errors are published, the effect size
estimate would be unbiased. In practice, we see scientists often do not simply
publish all results, but only statistically significant results in the desired
direction. An example of this is the literature on ego-depletion, where
hundreds of studies were published, most showing statistically significant
effects, but unbiased large scale replication studies revealed effect sizes of
0 </span><!--[if supportFields]><span lang=EN-US style='mso-ansi-language:EN-US'><span
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<span style='mso-element:field-separator'></span></span><![endif]--><span lang="NL" style="mso-ascii-font-family: Aptos; mso-hansi-font-family: Aptos;">(Hagger
et al., 2015; Vohs et al., 2021)</span><!--[if supportFields]><span lang=EN-US
style='mso-ansi-language:EN-US'><span style='mso-element:field-end'></span></span><![endif]--><span lang="EN-US" style="mso-ansi-language: EN-US;">. <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">What can be
done about the problem of biased effect sizes estimates if we mainly have access
to the studies that passed a significance filter? Statisticians have developed approaches
to adjust biased effect size estimates by taking a truncated distribution into
account </span><!--[if supportFields]><span lang=EN-US style='mso-ansi-language:
EN-US'><span style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>ADDIN ZOTERO_ITEM CSL_CITATION
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<span style='mso-element:field-separator'></span></span><![endif]--><span lang="EN-US" style="mso-ansi-language: EN-US; mso-ascii-font-family: Aptos; mso-hansi-font-family: Aptos;">(Taylor & Muller, 1996)</span><!--[if supportFields]><span
lang=EN-US style='mso-ansi-language:EN-US'><span style='mso-element:field-end'></span></span><![endif]--><span lang="EN-US" style="mso-ansi-language: EN-US;">. This approach has recently been
implemented in R </span><!--[if supportFields]><span lang=EN-US
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<span style='mso-element:field-separator'></span></span><![endif]--><span lang="EN-US" style="mso-ansi-language: EN-US; mso-ascii-font-family: Aptos; mso-hansi-font-family: Aptos;">(Anderson et al., 2017)</span><!--[if supportFields]><span
lang=EN-US style='mso-ansi-language:EN-US'><span style='mso-element:field-end'></span></span><![endif]--><span lang="EN-US" style="mso-ansi-language: EN-US;">. Implementing this approach in
practice is difficult, because we never know for sure if an effect size estimate
is biased, and if it is biased, how much bias there is. Furthermore, selection
based on significance is only one form of bias, whereas researchers who
selectively report significant results may engage in additional problematic
research practices, such as selectively reporting results, which are not
accounted for in the adjustment. Other researchers have referred to this
problem as a Type M error </span><!--[if supportFields]><span lang=EN-US
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Bayesian single and multiple comparison
procedures","volume":"15","author":[{"family":"Gelman","given":"Andrew"},{"family":"Tuerlinckx","given":"Francis"}],"issued":{"date-parts":[["2000",9,1]]},"citation-key":"gelman_type_2000"}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}
<span style='mso-element:field-separator'></span></span><![endif]--><span lang="EN-US" style="mso-ansi-language: EN-US; mso-ascii-font-family: Aptos; mso-hansi-font-family: Aptos;">(Gelman & Carlin, 2014; Gelman & Tuerlinckx,
2000)</span><!--[if supportFields]><span lang=EN-US style='mso-ansi-language:
EN-US'><span style='mso-element:field-end'></span></span><![endif]--><span lang="EN-US" style="mso-ansi-language: EN-US;"> and have suggested that researchers
always report the average inflation factor of effect sizes. I do not believe
this approach is useful. The Type M error is not an error, but a bias in
estimation, and it is more informative to compute the adjusted estimate based
on a truncated distribution as proposed by Taylor and Muller in 1996, than to compute
the average inflation for a specific study design. If effects are on average
inflated by a factor of 1.3 (the Type M error) it does not mean that the
observed effect size is inflated by this factor, and the truncated effect sizes
estimator by Taylor and Muller will provide researchers with an actual estimate
based on their observed effect size. Type M errors might have a function in
education, but they are not useful for scientists (I will publish a paper on Type S and M errors later this year, explaining in more detail why I think neither are useful concepts).<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Of course
the real solution to bias in effect size estimates due to significance filters
that lead to truncated or censored distributions is to stop selectively
reporting results. Designing highly informative studies that have high power to
both reject the null, as a smallest effect size of interest in an equivalence
test, is a good starting point. Publishing research as Registered Reports is
even better. Eventually, if we do not solve this problem ourselves, it is
likely that we will face external regulatory actions that force us to include
all studies that have received ethical review board approval to a public
registry, and update the registration with the effect size estimate, as is done
for clinical trials. <o:p></o:p></span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><br /></span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><i>References</i>:</span></p><p class="MsoBibliography"><!--[if supportFields]><span lang=EN-US
style='mso-ansi-language:EN-US'><span style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>ADDIN ZOTERO_BIBL
{"uncited":[],"omitted":[],"custom":[]}
CSL_BIBLIOGRAPHY <span style='mso-element:field-separator'></span></span><![endif]--><span lang="EN-US">Anderson, S. F., Kelley, K., & Maxwell, S. E.
(2017). Sample-size planning for more accurate statistical power: A method
adjusting sample effect sizes for publication bias and uncertainty. <i>Psychological
Science</i>, <i>28</i>(11), 1547–1562. https://doi.org/10.1177/0956797617723724<o:p></o:p></span></p><p class="MsoBibliography"><span lang="EN-US">Ensinck, E., & Lakens,
D. (2023). <i>An Inception Cohort Study Quantifying How Many Registered Studies
are Published</i>. PsyArXiv. https://doi.org/10.31234/osf.io/5hkjz<o:p></o:p></span></p><p class="MsoBibliography"><span lang="EN-US">Franco, A., Malhotra, N.,
& Simonovits, G. (2014). Publication bias in the social sciences: Unlocking
the file drawer. <i>Science</i>, <i>345</i>(6203), 1502–1505.
https://doi.org/10.1126/SCIENCE.1255484<o:p></o:p></span></p><p class="MsoBibliography"><span lang="EN-US">Gelman, A., & Carlin,
J. (2014). Beyond Power Calculations: Assessing Type S (Sign) and Type M
(Magnitude) Errors. <i>Perspectives on Psychological Science</i>, <i>9</i>(6),
641–651.<o:p></o:p></span></p><p class="MsoBibliography"><span lang="EN-US">Gelman, A., &
Tuerlinckx, F. (2000). Type S error rates for classical and Bayesian single and
multiple comparison procedures. <i>Computational Statistics</i>, <i>15</i>(3),
373–390. https://doi.org/10.1007/s001800000040<o:p></o:p></span></p><p class="MsoBibliography"><span lang="EN-US">Hagger, M. S.,
Chatzisarantis, N. L., Alberts, H., Anggono, C. O., Batailler, C., Birt, A.,
& Zwienenberg, M. (2015). A multi-lab pre-registered replication of the
ego-depletion effect. <i>Perspectives on Psychological Science</i>, 2.<o:p></o:p></span></p><p class="MsoBibliography"><span lang="EN-US">Sterling, T. D. (1959).
Publication decisions and their possible effects on inferences drawn from tests
of significance—Or vice versa. <i>Journal of the American Statistical
Association</i>, <i>54</i>(285), 30–34. JSTOR. https://doi.org/10.2307/2282137<o:p></o:p></span></p><p class="MsoBibliography"><span lang="EN-US">Taylor, D. J., &
Muller, K. E. (1996). Bias in linear model power and sample size calculation
due to estimating noncentrality. <i>Communications in Statistics-Theory and
Methods</i>, <i>25</i>(7), 1595–1610. https://doi.org/10.1080/03610929608831787<o:p></o:p></span></p><p class="MsoBibliography"><span lang="EN-US">Vohs, K. D., Schmeichel,
B. J., Lohmann, S., Gronau, Q. F., Finley, A. J., Ainsworth, S. E., Alquist, J.
L., Baker, M. D., Brizi, A., Bunyi, A., Butschek, G. J., Campbell, C., Capaldi,
J., Cau, C., Chambers, H., Chatzisarantis, N. L. D., Christensen, W. J., Clay,
S. L., Curtis, J., … Albarracín, D. (2021). A Multisite Preregistered
Paradigmatic Test of the Ego-Depletion Effect. </span><i><span lang="NL">Psychological
Science</span></i><span lang="NL">, <i>32</i>(10), 1566–1581. https://doi.org/10.1177/0956797621989733<o:p></o:p></span></p><p class="MsoNormal"><span lang="EN-US">
</span></p><p class="MsoNormal"><!--[if supportFields]><span lang=EN-US style='mso-ansi-language:
EN-US'><span style='mso-element:field-end'></span></span><![endif]--><span lang="EN-US"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><o:p> </o:p></span></p></div></div></div>Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com0tag:blogger.com,1999:blog-987850932434001559.post-50674171750188098512024-01-11T15:05:00.005+01:002024-01-11T15:14:00.653+01:00Surely God loves 51 km/h nearly as much as 49 km/h?<p>Next time
you get a fine for speeding, I suggest you try the following line of defense.
First, you explain to the judge that a speed limit of 50 km/h in densely populated
areas is a convention. It could just as easily have been set at 51 km/h, or 49 km/h. There is no bright line at 50 km/h that prevents all accidents, because
the number of deaths due to speeding is a continuous variable. Tell the judge
that you believe it is better if drivers ignore speed limits, and instead drive
in a <i>thoughtful</i>, <i>open</i>, and <i>modest</i> manner. Tell the judge
that, surely, God loves 51 kilometers per hour nearly as much as 49 kilometers
an hour. I strongly suspect the judge will roll their eyes, and instructs you
to pay the fine.</p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><o:p> </o:p></span></p><div class="separator" style="clear: both; text-align: center;"><span lang="EN-US" style="mso-ansi-language: EN-US;"><a href="https://blogger.googleusercontent.com/img/a/AVvXsEgIMAgdBRnuKJmWRm3tZWnyIbc7c9E7v9wUodAk2OdhF5TOIzA_UE5xcfU4a0T28EVQ15XRRNS6hE5fwrY9VYgG8Kk6r8Juf49kgSUwtMnB66A7CdCo1PmYg9O3IRJHLGS9UXIwc4_MYBtT4f8Q-9W4qeZ_ydHjR4Ec8wiZz2SGdINjYnFLo_VnoFquOPI" style="margin-left: 1em; margin-right: 1em;"><img alt="" data-original-height="2048" data-original-width="2048" height="240" src="https://blogger.googleusercontent.com/img/a/AVvXsEgIMAgdBRnuKJmWRm3tZWnyIbc7c9E7v9wUodAk2OdhF5TOIzA_UE5xcfU4a0T28EVQ15XRRNS6hE5fwrY9VYgG8Kk6r8Juf49kgSUwtMnB66A7CdCo1PmYg9O3IRJHLGS9UXIwc4_MYBtT4f8Q-9W4qeZ_ydHjR4Ec8wiZz2SGdINjYnFLo_VnoFquOPI" width="240" /></a></span></div><span lang="EN-US" style="mso-ansi-language: EN-US;"><br /></span><p></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">And yet,
when it comes to statistics, these are exactly the arguments statisticians
bring forward to criticize current rules in science that exist to regulate when
scientists can make claims based on tests. They will argue against dichotomous
decisions, and in favor of being “</span><span style="mso-ansi-language: #1000;">thoughtful,
open, and modest” </span><!--[if supportFields]><span style='mso-ansi-language:
#1000'><span style='mso-element:field-begin'></span><span
style='mso-spacerun:yes'> </span>ADDIN ZOTERO_ITEM CSL_CITATION
{"citationID":"bTft51NK","properties":{"formattedCitation":"(Wasserstein
et al., 2019)","plainCitation":"(Wasserstein et al.,
2019)","noteIndex":0},"citationItems":[{"id":2732,"uris":["http://zotero.org/users/371621/items/PWGKRSSX"],"itemData":{"id":2732,"type":"article-journal","container-title":"The
American
Statistician","DOI":"10.1080/00031305.2019.1583913","ISSN":"0003-1305","issue":"sup1","page":"1-19","source":"Taylor
and Francis+NEJM","title":"Moving to a World Beyond “p <
0.05”","volume":"73","author":[{"family":"Wasserstein","given":"Ronald
L."},{"family":"Schirm","given":"Allen
L."},{"family":"Lazar","given":"Nicole
A."}],"issued":{"date-parts":[["2019",3,29]]},"citation-key":"wasserstein_moving_2019"}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"}
<span style='mso-element:field-separator'></span></span><![endif]--><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Wasserstein
et al., 2019)</span><!--[if supportFields]><span style='mso-ansi-language:#1000'><span
style='mso-element:field-end'></span></span><![endif]--><span style="mso-ansi-language: #1000;">. </span><span lang="EN-US" style="mso-ansi-language: EN-US;">Statisticians
are like drivers. They deal with individual studies, like drivers deal with
their own car. Driving one kilometer faster or slower feels like an arbitrary
choice, just as how making a claim based on p < 0.06 or p < 0.04 is
arbitrary for a statistician. And in the individual world of drivers and
statisticians, there is no logical argument to treat driving 51 km/h at this
time and on this street differently from driving 49 km/h. <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Philosophers
of science are like the government. They do not deal with individual studies,
but they deal with the scientific system, just as governments deal with the
traffic management system. At this higher level it sometimes becomes necessary
to set rules, and enforce them. For example, as explained by the <a href="https://road-safety.transport.ec.europa.eu/system/files/2021-07/road_safety_thematic_report_speeding.pdf">European
Road Safety Observatory</a>, originally the driving speed was largely
determined by drivers, and fixed at the 85<sup>th</sup> percentile of the speed
on a road. If all drivers drive at a high speed, the maximum speed would be
high, if drivers drove at a low speed, the maximum speed would be lower. Such a
system is sometimes also advocated by statisticians: Let the community decide
what is best, without strict top-down rules. Regrettably, it is often not responsible
to let the community make their own rules. As the <a href="https://road-safety.transport.ec.europa.eu/system/files/2021-07/road_safety_thematic_report_speeding.pdf">European
Road Safety Observatory</a> notes: “However, many behavioural observations,
attention measurements, and the high number of traffic crashes caused by
excessive speed have shown that one cannot always rely on the judgement of
drivers to set a suitable speed limit.” <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The reason
why drivers can not determine how fast they should be allowed to drive is because
as a society we want to prevent accidents. The reward structure for drivers is
such that if they speed and do <i>not</i> get into an accident, they get to
where they want to be more quickly, and when they speed and<i> do</i> get into
an accident, they might kill a pedestrian or bicyclist. If we ignore having a
bad conscience (combined the inability of drivers to adequately estimate the
probability they will get into an accident), the reward structure would lead unacceptable
risks for pedestrians. If we left the criteria that allow a scientists to make
a claim up to scientists themselves, the reward structure would lead to unacceptable
rates of false claims.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">If someone
told you they were speeding to be on time for a meeting, you would likely not
scold them, or report them to the police. It is relatively accepted behavior,
at least when someone does not violate the speed limit by too much. According
to the <span style="mso-spacerun: yes;"> </span><a href="https://road-safety.transport.ec.europa.eu/system/files/2021-07/road_safety_thematic_report_speeding.pdf">European
Road Safety Observatory</a> “67% of Europeans admit to having speeded on rural
roads over the previous 30 days”. And yet, reducing the average driving speed
with 1 additional kilometer would save more than 2000 lives a year. Those small
violations that we find acceptable have real consequences that we are often not
aware of when we violate the rules. Scientists also admit to practices that
increase the probability of false claims, and no one will be fired for not
correcting for multiple comparisons, even if this in practice leads to a higher
Type 1 error rate than the 5% they say they will use to make scientific claims.
<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Enforcing
rules can prevent accidents and errors. And therefore, the driver who tries to
convince the judge that ‘surely God loves 51 km/h nearly as much as 49 km/h’
will have little success. The judge knows that enforcing violations saves
lives. In practice, drivers need to speed by more than 1 km/h to get a fine,
due to corrections for measurement uncertainty. In The Netherlands, 3 km is subtracted
from the speed measurement to guarantee a driver was speeding, given imperfect
measurement equipment. According to the <span style="mso-spacerun: yes;"> </span><a href="https://road-safety.transport.ec.europa.eu/system/files/2021-07/road_safety_thematic_report_speeding.pdf">European
Road Safety Observatory</a> “The detection equipment is often set in such a way
that there is a margin of tolerance with regard to the speed limit. The use of
such margins of tolerance serves to filter out minor, accidental violations and
to deal with the possible unreliability of the equipment. A disadvantage of
this approach is, however, that it strengthens drivers' opinion that a minor
offence is not so serious”. Similarly, in science, if we allow author to not
correct for multiple comparisons, or make claims based on ‘marginally
significant’ findings of p = 0.06 they might similarly feel the consequences
are not so serious. <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">But at the
system level, a Type 1 error rate of 10% instead of 5% has a massive impact on
the safety and efficiency of the scientific system. Whether acceptable safety
is reached by setting the alpha at 5% deserves to be empirically studied (just
as the acceptable driving speed is determined empirically). Just as driving
speeds, we might find different alpha levels acceptable in different research
lines. But that a driving speed has to be established and enforced will remain
important on a system level. <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Some drivers
will continue to complain about being fined for speeding, convinced as they are
that they can determine how fast they can drive at a specific time at a
specific road. Some people will never like being told what to do. Some
statisticians will continue to complain that they need to adhere to a 5% error
rate when making scientific claims, when they strongly believe that they can determine
when they should make a claim, and when not, on a case by case basis.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">We allow
drivers to voice their complaints, and if there are signals that traffic rules
lead to problems, they might be adjusted. And of course, no government is
perfect, so suboptimal decisions will sometimes be made. But we will never
abandon traffic rules, and will at best change the driving speed that will be
enforced, or the parts of the road where driving speeds will be enforced.
Similarly, we allow statisticians to complain about the use of significance
levels to make claims. But we will never abandon the use of enforced criteria
that regulate when scientists can make claims, and will at best change alpha
levels, or decrease the amount of research questions that test claims in favor
of descriptive research. When it comes to decisions about how we should
organize the traffic management system, we don’t ask drivers. Similarly, when
it comes to decisions about how to organize scientific knowledge generation, we
don’t ask statisticians. Scientific knowledge generation is studied by social
epistemologists. Science, like driving, is a social system with a specific
goal. It is of course beneficial if those government employees that create the
traffic management system are also drivers, just as it is useful if social
epistemologists understand statistics. But social epistemology is it’s own
specialization. <o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><o:p> </o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Some
scientists don’t like to think of science as a large ‘knowledge production
system’. Maybe it makes them feel like a cog in a machine. I like to think of
scientists as part of a system. Our jobs are very similar to garbage
collectors. It’s a large and essential system that exists because society needs
it and is willing to pay for it, that aims to achieve a goal efficiently, with
a strong social component. Therefore, it makes sense to me that science needs a
set of rules to reduce errors in the system. Not all scientists will agree,
just as not all civilians agree with the government. From a statistical perspective,
there might not be a difference between driving 49 or 51 km/h, but from a
social epistemological perspective it is justifiable to fine a driver if they drive
51 km/h inside city limits, and not fine them if they drive 49 km/h. <o:p></o:p></span></p>Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com0tag:blogger.com,1999:blog-987850932434001559.post-15021240095869276412023-08-23T09:03:00.016+02:002023-08-23T11:48:21.499+02:00Reflections on the viral thread by Dr Louise Raw spreading fake news about unethically performed radiation experiments on Punjabi women in the 1950's<p><span lang="EN-US" style="mso-ansi-language: EN-US;">On the 19<sup>th</sup>
of August, Dr Louise Raw wrote a series of tweets that spread fake news about
unethically performed radiation experiments on Punjabi women in the 1950’s. The
tweet went viral, and I saw many academics I follow on Twitter uncritically retweet
this fake news. </span></p>
<p class="MsoNormal"><img alt="" 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" /><span lang="EN-US" style="mso-ansi-language: EN-US;"><span style="mso-spacerun: yes;"> <br /></span></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">I read the
thread. It is well written – a masterclass in the spread of fake news. It starts
with kindness, only to later spring the trap of abuse. As you read the thread,
you will be surprised, and your emotions will get the better of you. It is very
effective. </span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">But then we
come to the main claim. </span><span lang="EN-US" style="mso-ansi-language: EN-US;"><br /></span></p>
<p class="MsoNormal"><img alt="" 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" /></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">When I read this, alarm bells go off. First of all, the
main source is a filmmaker. That is an unreliable source for me. Some
filmmakers of course tell the truth, but I have also seen a massive amount of
documentaries that twist facts – after all, these people need to make money by
selling their documentary, and the truth does not always sell. Now, if this
really was unethical research of Tuskegee Syphilis level (<a href="https://www.cdc.gov/tuskegee/timeline.htm">https://www.cdc.gov/tuskegee/timeline.htm</a>)
you would have both the truth, and a best selling documentary. So, it can be
true, but it starts with a very weak source. </span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">T</span><span lang="EN-US" style="mso-ansi-language: EN-US;">he second
point in this tweet (screenshot above) about the chapatis almost made me laugh out loud, it is this silly to me. They
performed this experiment to examine effects of radioactive food, choosing
Punjabi women, and feeding them radioactive chapatis. Now, maybe it is because
I do experiments, and I like to do them efficiently. But if I want to
unethically test the effect of radioactive food, I would go to a prison, or a
mental health facility, where people are already getting food served to them. I
don’t try to find a community of Punjabi women, and bake them chapatis. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">At this
point, I do not trust this thread and the claims. This sounds ridiculously
implausible. And when things are ridiculously implausible, they often are lies.
I evaluate the probability that some activist like Louis Raw would fall for
such a lie – and I think that probability is extremely high. </span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">A logical
thing to do now is search for other sources. Surely, someone must have written
about this somewhere. One of the first articles I find is <a href="https://www.independent.co.uk/news/britons-used-in-radiation-tests-affected-200-1353929.html">https://www.independent.co.uk/news/britons-used-in-radiation-tests-affected-200-1353929.html</a>. </span><span class="css-901oao css-16my406 r-poiln3 r-bcqeeo r-qvutc0">It says the claims were made by the "Campaign for Nuclear Disarmament</span><span lang="EN-US" style="mso-ansi-language: EN-US;">", an <a href="https://en.wikipedia.org/wiki/Campaign_for_Nuclear_Disarmament">activist group</a> </span>founded in 1957 in the wake of widespread fear of nuclear conflict and the effects of nuclear tests. I reduce my confidence in the accusations even more - activists can be right, but my prior is there is also great risk of bias. A second source I find is<span lang="EN-US" style="mso-ansi-language: EN-US;"> <a href="https://www.jstor.org/stable/25179350">https://www.jstor.org/stable/25179350</a>.
The title ‘MRC cleared of unethical research practices’ reduced my belief in
the claims even more. I know that it is possible there 1) was an unethical
experiment, and 2) an investigation into the unethical experiment suffered from
a huge conspiracy level cover up. But, for example, when a press story about
the Syphilis Study at Tuskegee broke in 1972 the independent committee
investigating it just found clear unethical conduct and said so. I would now
need to believe researchers performed an unnecessarily complex study, AND there
was a government cover up. That is getting a bit too much for me.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The
investigation revealed one act of unethical behavior, where the doctors removed
the femora of a baby that had died and the mother was refused the body of the
baby. That sounds unacceptable to me, but it has nothing to do with the claims
of the use of radiation. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The article
provides a great possibility to quote out of context when it says “</span><span style="mso-ansi-language: #1000;">The inquiry concluded: "Many of the
studies would not be considered acceptable now</span><span style="mso-ansi-language: #1000;">"</span><span lang="EN-US" style="mso-ansi-language: EN-US;">”. But what is not deemed acceptable now is
that participants were not updated about the results of the study – again, this
has nothing to do with radiation. </span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><img alt="" 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" /> <br /></span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Another
article reporting on this outcome summarized it as follows “</span><span style="mso-ansi-language: #1000;">But the five-member committee did</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">conclude
that some of the participants</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">had been caused long-term worry and</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">distress,
which "could have been</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">avoided by better communication by researchers</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">during
the</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">studies, and by ensuring</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">information on the</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">study
was readily available</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">afterwards."</span><span style="mso-ansi-language: EN-US;"> <span lang="EN-US"><a href="https://pubmed.ncbi.nlm.nih.gov/9662351/">https://pubmed.ncbi.nlm.nih.gov/9662351/</a>
</span></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">I started
to tweet my conclusion that this was a fake news story. Richard van Noorden
provided me with an additional link: <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2550248/">https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2550248/</a></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">This piece
is written by K E Halnan, the doctor involved in the study. He studied cancer
and iodines and published over 70 papers in his lifetime on related topics. His obituary
states “</span><span style="mso-ansi-language: #1000;">Having seen</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">the
horrors of war, he resolved to take up medicine in order</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">“to
make more of a difference”.</span><span lang="EN-US" style="mso-ansi-language: EN-US;">” <a href="https://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=web&cd=&ved=0CDgQw7AJahcKEwiIrJarl_KAAxUAAAAAHQAAAAAQAw&url=https%3A%2F%2Fwww.hkmj.org%2Fsystem%2Ffiles%2Fhkm0604p167.pdf&psig=AOvVaw1IMnjmjA-pDSOjph0mBvTw&ust=1692859421418159&opi=89978449">https://www.hkmj.org/system/files/hkm0604p167.pdf</a></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><img alt="" 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" /></span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Doctor
Halnan was upset when the documentary was shown on Channel 4 on July 6,
1995. To be clear, this story about unethical conduct was not hidden away for
the last 30 years – it was broadcasted on prime time, and caused a great stir.
The reason it is not remembered, is because later investigations showed it was
not true. I think if you get accused by people who misrepresent the research you have done, you would be upset enough to write a letter about it as well. Note there was no reason for Halnan to write this personal letter where he identifies himself as one of the researchers. He could have remained anonymous.<br /></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Doctor
Halnan tried to explain why the study was performed. The experiment was
performed out of concern over widespread anaemia among Asian women due to iron
deficiency due to traditional food this population ate. For me, this is a much
more logical reason to do a study in Punjabi women and baking them chapatis. </span></p><p><img alt="" 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" /> <br /></p><p></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">I am not
writing this blog to accuse anyone for making a fool of themselves. It is true that I was
disappointed in the many academics I saw retweeting this fake news. It also
reminded me we need to do a lot more to train critical thinking skills in
people. Maybe this blog can help a little in training people to evaluate whether a tweet contains fake news. </span></p>
Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com1tag:blogger.com,1999:blog-987850932434001559.post-2537243056370788072023-08-09T13:41:00.005+02:002023-08-10T10:21:07.646+02:00How I tried to get a paper that I own retracted: A journey and call to action against Prime Scholars<div> This is a Guest Post by <a href="https://nnnvd.wordpress.com/">Noah van Dongen</a><br /><br /> <br /><hr id="docs-internal-guid-5620c441-7fff-5337-402e-c581e0cd33ad" /><div style="text-align: right;"><i><span style="font-size: x-small;">Right after the replication emergency in mental science, numerous clinicians have embraced rehearses that support the vigor and straightforwardness of the logical cycle, including preregistration, information sharing, code sharing, and enormous reproducibility studies</span></i></div><div style="text-align: right;"><i><span style="font-size: x-small;"> </span><br /></i></div></div><div style="text-align: right;"><span style="font-size: x-small;">not Noah van Dongen, but somebody else (2022)</span><br /></div><div><hr id="docs-internal-guid-5620c441-7fff-5337-402e-c581e0cd33ad" /><br />This is a true story about how I tried to get a paper that I own retracted. The copyright of the paper in question is attributed to me. Several requests and demands for removal were issued, but the journal has not yet fully complied. Of course, this might be due to the fact that I did not write the paper, the paper is an incoherent mess of academic gibberish, and Prime Scholars is the publisher of the journal. This post tells my story. It ends with a call to action against Prime Scholars and similar outfits. <br /><br />For those of you who are unfamiliar with Prime Scholar, this is what <a href="https://en.wikipedia.org/wiki/Prime_Scholars">Wikipedia</a> has to say about them:<br /><br /><img height="240" src="https://lh5.googleusercontent.com/ZkF7SRSG1qLDgjfCruRvEg9T4VjwNbyZuLpwg2MhmW_-kKx1WKcVq9b2brgfv1XJhhtJhAuBcVHXZTKsr5Ie7ccAMJ-XTub15aoosEA0sDeBDzjZ7bicoT2jRPJlm2YiC2lFKxGblJeNMaVaeDPnDW0=w640-h240" width="640" /> <br /><br /><br />As <a href="https://twitter.com/deevybee/status/1608424393683914755">Bishop notes</a>, some famous, though long dead, authors are counted among the people that publish in Prime Scholars’ journals.<a href="#1" name="top1"><sup>1</sup></a><br /><br /><br /><img height="485" src="https://lh4.googleusercontent.com/bv1jtySy1EjHdnsnCBSshv2QWlO6iKfENjvyB_bTMmq8ZOgj83_0DGgpcZPqiDc-AfKfcuCa05RvCfG4gzSzGjBgfTE_iaYbtqLGSdBSP8wjszcP7edAsDnc7UiXfqm-07xI0zriEMq-k9x_HVMeh5E=w640-h485" width="640" /><br /><br /><br />Apparently, I can now count myself among the illustrious company of Hesse, Bronte, and Whitman. For I too, without my knowledge and against my wishes, am now a scholar that published a <i>word soup essay</i> in a Prime Scholar journal.</div><div><br /><h4 style="text-align: left;">The paper that I never wrote</h4> </div><div>I will not keep you in suspense any longer and tell you which journal and publication I am talking about. <a href="https://www.primescholars.com/acta-psychopathology/archive.html">Acta Psychopathologica</a> is one of the 56 journals owned by Prime Scholars. It claims to have a Journal Impact Factor of 2.15 (or 2.4 according to their <a href="https://www.primescholars.com/acta-psychopathology.html">about page</a>) and has 524 citations reported on Google Scholar (or 447 according to <a href="https://scholar.google.com/citations?hl=en&user=UUoZk0oAAAAJ&scilu=&scisig=AMD79ooAAAAAYSxXu_cWKTuY7vWfA-fiMsybfEKbMXhu&gmla=AJsN-F6LhGIu4nINpl9ihdHfVK2QTejUTB3U060M8BMl48KLCT2e1nD9tUz_5WNK1P7GwCWV0LxG4o1BJ6gsvuV4wOuizRzY2rYh6-eagWefjpoH2jUjUiE&sciund=5029928806789923566">Google Scholar</a>). The journal has published 9 volumes, with some as many as 9 issues, and a total of 9 special issues. The <i>academic trainwreck</i> attributed to me was published in May 2022, as part of the fourth issue of volume 8. There are four other publications in this issue. For as far as I can tell, all of their authors are existing and living scientists. And all publications are of similar quality: syntactically it looks like English, but it reads like a bad acid trip (or the ramblings of a drunk philosopher). <br /><br />And now, the <i>word vomit</i> in question. The<i> literary disaster</i> is titled “<a href="https://www.primescholars.com/articles/phenomena-can-be-characterized-as-general-patterns-in-observations-of-psychology-theories.pdf">Phenomena can be Characterized as General Patterns in Observations of Psychology Theories.</a>” and is attributed to a single author, Noah Van Dongen. The efficiency of the review process is astounding:</div><div><br /></div><div style="text-align: left;"><ul id="docs-internal-guid-4168b947-7fff-57d2-d7a8-341e90541897" style="margin-bottom: 0px; margin-top: 0px; padding-inline-start: 48px;"><li aria-level="1" dir="ltr" style="background-color: transparent; color: black; font-family: Arial, sans-serif; font-style: normal; font-variant: normal; font-weight: 400; list-style-type: disc; text-decoration: none; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span face="Arial,sans-serif" style="background-color: transparent; color: black; font-size: small; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;">1 April 2022: submission (nice touch on April fools)</span></p></li><li aria-level="1" dir="ltr" style="background-color: transparent; color: black; font-family: Arial, sans-serif; font-style: normal; font-variant: normal; font-weight: 400; list-style-type: disc; text-decoration: none; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span face="Arial,sans-serif" style="background-color: transparent; color: black; font-size: small; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;">4 April 2022: editor assigned</span></p></li><li aria-level="1" dir="ltr" style="background-color: transparent; color: black; font-family: Arial, sans-serif; font-style: normal; font-variant: normal; font-weight: 400; list-style-type: disc; text-decoration: none; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span face="Arial,sans-serif" style="background-color: transparent; color: black; font-size: small; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;">18 April 2022: reviewed</span></p></li><li aria-level="1" dir="ltr" style="background-color: transparent; color: black; font-family: Arial, sans-serif; font-style: normal; font-variant: normal; font-weight: 400; list-style-type: disc; text-decoration: none; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span face="Arial,sans-serif" style="background-color: transparent; color: black; font-size: small; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;">25 April 2022: revised</span></p></li><li aria-level="1" dir="ltr" style="background-color: transparent; color: black; font-family: Arial, sans-serif; font-style: normal; font-variant: normal; font-weight: 400; list-style-type: disc; text-decoration: none; vertical-align: baseline; white-space: pre;"><p dir="ltr" role="presentation" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt; text-align: justify;"><span face="Arial,sans-serif" style="background-color: transparent; color: black; font-size: small; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;">2 May 2022: published</span></p></li></ul><p></p></div><div style="text-align: left;">One month and a day between submission and publication! Receiving and correcting proofs from a journal’s copy editor usually takes me more than a month! It is also very neat that, apart from the submission to editor assignment, everything else was done in exactly a week; like clockwork! I wish my colleagues and I could work this organized and systematic. The <i>terrible text(ual) trifle</i><a href="#2" name="top2"><sup>2</sup></a> cannot be found on Google Scholar, it does not show up in the Google Scholar page of the journal, and its DOI does not exist. Services, like Google Scholar and Researchgate, who typically notify me when something academic is published under my name, have not picked up on this addition to my oeuvre. The only reason I know of its existence is that a colleague came across it in a Google search for the definition of “phenomena”. <br /><br />About the content of the <i>linguistic barf</i>, just like the other papers in Issue 4 of Volume 8, it consists of a collection of English sentences that appear to approach syntactic correctness, though devoid of any clear meaning. The only thing it has going for it, is that it is blessedly short. It is good to read that there were no conflicts of interest, but it’s too bad that they misspelled my name. In the Netherlands, our surnames can have prefixes, like “van” or “van der” (which translates to “from”), which are not capitalized. My surname is spelled “van Dongen” not “Van Dongen”. Just pointing this out. However, considering the quality of the commentary, it is surprising they got so close to getting my personal information correct. Yes, this verbose vacuity is published as a commentary, though for the life of me I cannot figure out what it is supposed to be commenting on.<br /><br />Trying to make sense of the <i>senseless drivel</i>, it seems like the true authors have taken part of the introduction from a preprint (that I did write) and ran it through a thesaurus to avoid being flagged for plagiarism, creating what is called <a href="https://www.nature.com/articles/d41586-021-02134-0">tortured phrases</a>. The paper that they used as the mold seems to be an early version of <a href="https://psyarxiv.com/qd69g/https://psyarxiv.com/qd69g/">Productive Explanation: A Framework for Evaluating Explanations in Psychological Science</a>, which was posted on PsyArXiv on 13 April 2022<a href="#3" name="top3"><sup>3</sup></a>. For comparison, here are the first sentences of the original and the forgery, respectively:<br /><br /><blockquote>In the wake of the replication crisis in psychological science, many psychologists have adopted practices that bolster the robustness and transparency of the scientific process, including preregistration (Chambers, 2013), data sharing (Wicherts et al., 2006), code sharing, and massive reproducibility studies (e.g., Aarts et al., 2015; Walters, 2020).<br /><br />Right after the replication emergency in mental science, numerous clinicians have embraced rehearses that support the vigor and straightforwardness of the logical cycle, including preregistration, information sharing, code sharing, and enormous reproducibility studies.</blockquote><br />I did not take the time to figure out which other sentences they used for the figurative butchery (or is ‘literal’ more appropriate here?) and what kind of procedure they used to end up with this collection of sentences. It looks like they just selected a certain part of the introduction, but I am not sure.</div><div style="text-align: left;"><br /><h4>The process of getting the paper retracted and succeeding partially </h4> </div><div style="text-align: left;">I think this is enough for setting the stage. Let me now tell you about my journey of getting the <i>verbal catastrophe</i> retracted, of which I explicitly own the copyrights. This story started in November of 2022 when a colleague stumbled across the <i>linguistic salad</i>. Ironically enough, we were working on the original, wanting to improve our definition of phenomena, and searching for definitions by others that we might be able to use. It is quite a surreal experience to see your credentials on work you know you didn’t produce while simultaneously searching your memory for what you could have done to make this happen. <br /><br />As a true academic, I acted immediately, ten days later. My first attempt was emailing the journal requesting the removal of the <i>textual horror</i>. This was on 8 December 2022.<a href="#4" name="top4"><sup>4</sup></a> I gave the journal ample time to respond (or I forgot about this problem due to other stuff that was going on at the moment). But, after three months without reply, I contacted the legal team of the University of Amsterdam. They were very understanding and wanted to be of assistance. <br /><br />On 11 2023, the UvA’s legal team emailed Acta Psychopathologica demanding the retraction of the <i>atrocious article</i> and threatened legal action if they did not comply. Acta Psychopathologica did not respond to this email either. On 20 April, the legal team sent a reminder, to which they also did not reply. At the moment, Acta Psychologica has not responded to any of our messages. <br /><br />The UvA’s legal team had also advised me to report the identity theft to the <i>Rijksdienst Identiteitsgegevens</i> (RVIG; translation: the identity data safety services of the Dutch government), which I did on 11 April 2023. Conveniently, the RVIG has an online form for this. However, identity theft is usually about the illegitimate use of your credit cards or passports. It was more than a bit awkward to write about how you have been impersonated to publish shoddy scientific work in (a website that calls itself a) journal. Nine days after I submitted the form, the RVIG contacted me. They were very understanding, but told me there was nothing they could do for me. They advised me to report the crime to the police. <br /><br />Again, other responsibilities got in the way. About a month later, I called the police on 1 June 2023. The central operator noted down the specifics of my predicament and told me that the department responsible for identity theft would contact me to make an appointment, which they did on Saturday 3 June 2023. As it turns out, reporting identity theft must be done in person at the police station. Maybe this is to make sure that it is actually you that is reporting the theft of your identity. On 13 June 2023, I went to my appointment to report the crime, which took about an hour. The officer taking my report was friendly and understanding. Actually, she was very understanding considering the curiousness of the situation. Noteworthy about this experience, is that she wrote the report from my (first person) perspective, but in her own words. There are many new experiences I am gaining throughout this journey, and this was one of the strange ones. Reading something as if you said it, correct in terms of content, though not in a way that you would say, is surreal to say the least. You are instantly aware of your own idiolect. Or at least, that is what I experienced. <br /><br />A week later, I informed the UvA’s legal team of the police report. They promptly sent another email to Acta Psychopathologica requesting them again to remove the semantic puree, though this time adding that the crime had been reported to the police and that legal action would follow. <br /><br />The UvA’s legal team also contacted the Editors-in-Chief of Acta Psychopathologica to request them to remove the language pit stain. They received two replies to this request, which can be summarized as: I did not actually do anything for this journal and I would like to be removed from the editorial board. <br /><br />On 7 July 2023 the <i>horrendous word swirl</i> was no longer visible on the website. There are now only four papers in <a href="https://www.primescholars.com/archive/ipap-volume-8-issue-4-year-2022.html">Volume 8 Issue 4</a> instead of five. However, the <a href="https://www.primescholars.com/articles/phenomena-can-be-characterized-as-general-patterns-in-observations-of-psychology-theories.pdf">pdf version of the paper</a> can still be reached and I am still listed as an author on the <a href="https://www.primescholars.com/author/noah-van-dongen-35156">Prime Scholars website</a>.<br /><br /><h4>What is next?</h4> </div><div style="text-align: left;">My paper is not the only instance of identity theft in Acta Psychopathologica and other Prime Scholars journals. Currently, I am contacting the other authors in Acta Psychopathologica one by one to ask if they wrote the paper that is attributed to them but this is a slow process. My aim is to make them aware of the fraud and start requesting the traction of the fraudulent papers. <br /><br />I’ve also come across <a href="https://www.timeshighereducation.com/news/identity-theft-victims-back-legal-action-against-journals">this article</a> in The Times Higher Education, which mentions legal actions being undertaken. I’m trying to find out if legal actions against Prime Scholars are indeed in the works. If so, I hope I can join them. If not, I want to get them started. <br /><br /><h4>What you can do to help</h4><ol style="text-align: left;"><li>Check if you or people you know have papers attributed to them in a Prime Scholar journal. </li><li>Contact me if you are also a victim of identity theft and want to join legal actions against Prime Scholars.</li><li>Share this story and (ask people to) take action against Prime Scholars.</li></ol>Generating academic articles that appear authentic has become much easier now large language models like ChatGPT have arrived on the scene. I think it is safe to assume that we don’t want fake papers to start invading our academic corpusses; devaluing our work and eroding the public’s trust in science. This does not seem to be a problem that will solve itself. We need to take active steps against these practices and we need to act now!<br /><br /><h4 style="text-align: left;">One last thought about publishers</h4><br />Don’t you think that ‘respectable’ academic publishers should accept some responsibility for this predicament we find ourselves? If outfits like Prime Scholars are making a mockery of their business and start poisoning the well, should they not also undertake (legal) steps to protect their profession? </div><div> </div><div> </div>
<span style="font-size: x-small;"><a name="1"><b>1 </b></a> Also, see <a href="https://retractionwatch.com/2022/12/29/meet-the-publisher-making-the-science-of-bronte-faulkner-and-whitman-available-for-the-first-time/" target="_blank">this article on Retraction Watch</a><a href="#top1"><sup>↩</sup></a><br /></span>
<span style="font-size: x-small;"><a name="2"><b>2 </b></a>For the interested, a trifle is a layered dessert of English origin. In Friends’ episode 9 of season 6, Rachel tries to make this dessert for Thanksgiving and accidentally adds beef sautéed with peas and onion to the dessert. The result still sounds better than the paper in question.<a href="#top2"><sup>↩</sup></a><br /></span>
<span style="font-size: x-small;"><a name="3"><b>3 </b></a>The astute reader realized that this is 12 days after it was supposedly submitted to Acta Psychopathologica. For everybody else is now also aware due to this informative footnote.<a href="#top3"><sup>↩</sup></a><br /></span>
<span style="font-size: x-small;"><a name="4"><b>4 </b></a>I know you Americans like to put the month in front of the date, that just looks wrong.<a href="#top4"><sup>↩</sup></a></span><br />
Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com0tag:blogger.com,1999:blog-987850932434001559.post-82710873404125891582023-04-13T10:16:00.004+02:002023-04-13T15:37:10.479+02:00Preventing common misconceptions about Bayes Factors<p><span style="mso-ansi-language: #1000;">As more people have
started to use Bayes Factors, we should not be surprised that misconceptions
about Bayes Factors have become common. A recent study shows that the
percentage of scientific articles that draw incorrect inferences based on
observed Bayes Factors is distressingly high (Wong et al., 2022), with 92% of
articles demonstrating at least one misconception of Bayes Factors. Here I will
review some of the most common misconceptions, and how to prevent them.</span>
</p><p class="MsoNormal"><b><span style="mso-ansi-language: #1000;">Misunderstanding 1:
Confusing Bayes Factors with Posterior Odds.</span></b></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">One common criticism
by Bayesians of null hypothesis significance testing (NHST) is that NHST
quantifies the probability of the data (or more extreme data), given that the
null hypothesis is true, but that scientists should be interested in the
probability that the hypothesis is true, given the data. Cohen (1994) wrote:</span></p>
<p class="MsoNormal"><i><span style="mso-ansi-language: #1000;">What’s wrong with
NHST? Well, among many other things, it does not tell us what we want to know,
and we so much want to know what we want to know that, out of desperation, we
nevertheless believe that it does! What we want to know is “Given these data,
what is the probability that Ho is true?”</span></i></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">One might therefore
believe that Bayes factors tell us something about the probability that a
hypothesis true, but this is incorrect. A Bayes factor quantifies how much we
should update our belief in one hypothesis. If this hypothesis was extremely
unlikely (e.g., the probability that people have telepathy) this hypothesis
might still be very unlikely, even after computing a large Bayes factor in a
single study demonstrating telepathy. If we believed the hypothesis that people
have telepathy was unlikely to be true (e.g., we thought it was 99.9% certain
telepathy was not true) evidence for telepathy might only increase our belief
in telepathy to the extent that we now believe it is 98% unlikely. The Bayes
factor only corresponds to our posterior belief if we were perfectly uncertain
about the hypothesis being true or not. If both hypotheses were equally likely,
and a Bayes factor indicates we should update our belief in such a way that the
alternative hypothesis is three times more likely than the null hypothesis,
only then would we end up believing the alternative hypothesis is exactly three
times more likely than the null hypothesis. One should therefore not conclude
that, for example, given a BF of 10, the alternative hypothesis is more likely
to be true than the null hypothesis. The correct claim is that people should
update their belief in the alternative hypothesis by a factor of 10.</span></p>
<p class="MsoNormal"><b><span style="mso-ansi-language: #1000;">Misunderstanding 2:
Failing to interpret Bayes Factors as relative evidence.</span></b></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">One benefit of Bayes
factors that is often mentioned by Bayesians is that, unlike NHST, Bayes
factors can provide support for the null hypothesis, and thereby falsify
predictions. It is true that NHST can only reject the null hypothesis, although
it is important to add that in frequentist statistics equivalence tests can be
used to reject the alternative hypothesis, and therefore there is no need to
switch to Bayes factors to meaningfully interpret the results of
non-significant null hypothesis tests.</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Bayes factors quantify
support for one hypothesis relative to another hypothesis. As with likelihood
ratios, it is possible that one hypothesis is supported more than another
hypothesis, while both hypotheses are actually false. It is incorrect to
interpret Bayes factors in an absolute manner, for example by stating that a
Bayes factor of 0.09 provides support for the null hypothesis. The correct
interpretation is that the Bayes factor provides relative support for H0
compared to H1. With a different alternative model, the Bayes factor would
change. As with a signiifcant equivalence tests, even a Bayes factor strongly
supporting H0 does not mean there is no effect at all - there could be a true,
but small, effect.</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">For example, after
Daryl Bem (2011) published 9 studies demonstrating support for pre-cognition
(conscious cognitive awareness of a future event that could not otherwise be
known) a team of Bayesian statisticians re-analyzed the studies, and concluded
“Out of the 10 critical tests, only one yields “substantial” evidence for H1,
whereas three yield “substantial” evidence in favor of H0. The results of the
remaining six tests provide evidence that is only “anecdotal”” (2011). In a
reply, Bem and Utts (2011) reply by arguing that the set of studies provide
convincing evidence for the alternative hypothesis, if the Bayes factors are
computed as relative evidence between the null hypothesis and a more
realistically specified alternative hypothesis, where the effects of
pre-cognition are expected to be small. This back and forth illustrates how
Bayes factors are relative evidence, and a change in the alternative model
specification changes whether the null or the alternative hypothesis receives
relatively more support given the data.</span></p>
<p class="MsoNormal"><b><span style="mso-ansi-language: #1000;">Misunderstanding 3:
Not specifying the null and/or alternative model.</span></b></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Given that Bayes
factors are relative evidence for or against one model compared to another
model, it might be surprising that many researchers fail to specify the alternative
model to begin with when reporting their analysis. And yet, in a systematic
review of how psychologist use Bayes factors, van de Schoot et al. (2017) found
that “31.1% of the articles did not even discuss the priors implemented”. Where
in a null hypothesis significance test researchers do not need to specify the
model that the test is based on, as the test is by definition a test against an
effect of 0, and the alternative model consists of any non-zero effect size (in
a two-sided test), this is not true when computing Bayes factors. The null
model when computing Bayes factors is often (but not necessarily) a point null
as in NHST, but the alternative model only one of many possible alternative
hypotheses that a researcher could test against. It has become common to use
‘default’ priors, but as with any heuristic, defaults will most often give an
answer to a nonsensical question, and quickly become a form of mindless
statistics. When introducing Bayes factors as an alternative to frequentist
t-tests, Rouder et al. (2009) write:</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">This commitment to
specify judicious and reasoned alternatives places a burden on the analyst. We
have provided default settings appropriate to generic situations. Nonetheless,
these recommendations are just that and should not be used blindly. Moreover,
analysts can and should consider their goals and expectations when specifying
priors. Simply put, principled inference is a thoughtful process that cannot be
performed by rigid adherence to defaults.</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">The priors used when
computing a Bayes factor should therefore be both specified and justified.</span></p>
<p class="MsoNormal"><b><span style="mso-ansi-language: #1000;">Misunderstanding 4:
Claims based on Bayes Factors do not require error control.</span></b></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">In a paper with the
provocative title “Optional stopping: No problem for Bayesians” Rouder (2014)
argues that “Researchers using Bayesian methods may employ optional stopping in
their own research and may provide Bayesian analysis of secondary data regardless
of the employed stopping rule.” If one would merely read the title and
abstract, a reader might come to the conclusion that Bayes factors a wonderful
solution to the error inflation due to optional stopping in the frequentist
framework, but this is not correct (de Heide & Grünwald, 2017).</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">There is a big caveat
about the type of statistical inferences that is unaffected by optional
stopping. Optional stopping is no problem for Bayesians if they refrain from
making a dichotomous claim about the presence or absence of an effect, or when
they refrain from drawing conclusions about a prediction being supported or
falsified. Rouder notes how “Even with optional stopping, a researcher can
interpret the posterior odds as updated beliefs about hypotheses in light of
data.” In other words, even after optional stopping, a Bayes factor tells
researchers who much they should update their belief in a hypothesis.
Importantly, when researchers make dichotomous claims based on Bayes factors
(e.g., “The effect did not differ significantly between the condition, BF10 =
0.17”) then this claim can be correct, or an error, and error rates become a
relevant consideration, unlike when researchers simply present the Bayes factor
for readers to update their personal beliefs.</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Bayesians disagree
among each other about whether Bayes factors should be the basis of dichotomous
claims, or not. Those who promote the use of Bayes factors to make claims often
refer to thresholds proposed by Jeffreys (1939), where a BF > 3 is
“substantial evidence”, and a BF > 10 is considered “strong evidence”. Some
journals, such as Nature Human Behavior, have the following requirement for
researchers who submit a Registered Report: “For inference by Bayes factors,
authors must be able to guarantee data collection until the Bayes factor is at
least 10 times in favour of the experimental hypothesis over the null
hypothesis (or vice versa).” When researchers decide to collect data until a
specific threshold is crossed to make a claim about a test, their claim can be
correct, or wrong, just as when p-values are the statistical quantity a claim
is based on. As both the Bayes factor and the p-value can be computed based on
the sample size and the t-value (Francis, 2016; Rouder et al., 2009), there is
nothing special about using Bayes factors as the basis of an ordinal claim. The
exact long run error rates can not be directly controlled when computing Bayes
factors, and the Type 1 and Type 2 error rate depends on the choice of the
prior and the choice for the cut-off used to decide to make a claim.
Simulations studies show that for commonly used priors and a BF > 3 cut-off
to make claims the Type 1 error rate is somewhat smaller, but the Type 2 error
rate is considerably larger (Kelter, 2021).</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">To conclude this
section, whenever researchers make claims, they can make erroneous claims, and
error control should be a worthy goal. Error control is not a consideration
when researchers do not make ordinal claims (e.g., X is larger than Y, there is
a non-zero correlation between X and Y, etc). If Bayes factors are used to
quantify how much researchers should update personal beliefs in a hypothesis,
there is no need to consider error control, but researchers should also refrain
from making any ordinal claims based on Bayes factors in the results section or
the discussion section. Giving up error control also means giving up claims
about the presence or absence of effects.</span></p>
<p class="MsoNormal"><b><span style="mso-ansi-language: #1000;">Misunderstanding 5:
Interpret Bayes Factors as effect sizes.</span></b></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Bayes factors are not
statements about the size of an effect. It is therefore not appropriate to
conclude that the effect size is small or large purely based on the Bayes
factor. Depending on the priors used when specifying the alternative and null
model, the same Bayes factor can be observed for very different effect size
estimates. The reverse is also true. The same effect size can correspond to
Bayes factors supporting the null or the alternative hypothesis, depending on
how the null model and the alternative model are specified. Researchers should
therefore always report and interpret effect size measure. Statements about the
size of effects should only be based on these effect size measures, and not on
Bayes factors.</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Any tool for
statistical inferences will be mis-used, and the greater the adoption, the more
people will use a tool without proper training. Simplistic sales pitches for
Bayes factors (e.g., Bayes factors tell you the probability that your
hypothesis is true, Bayes factors do not require error control, you can use
‘default’ Bayes factors and do not have to think about your priors) contribute
to this misuse. When reviewing papers that report Bayes factors, check if the
authors use Bayes factors to draw correct inferences.</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;"> </span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Bem, D. J. (2011).
Feeling the future: Experimental evidence for anomalous retroactive influences
on cognition and affect. Journal of Personality and Social Psychology, 100(3),
407–425. https://doi.org/10.1037/a0021524</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Bem, D. J., Utts, J.,
& Johnson, W. O. (2011). Must psychologists change the way they analyze
their data? Journal of Personality and Social Psychology, 101(4), 716–719.
https://doi.org/10.1037/a0024777</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Cohen, J. (1994). The
earth is round (p .05). American Psychologist, 49(12), 997–1003.
https://doi.org/10.1037/0003-066X.49.12.997</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">de Heide, R., &
Grünwald, P. D. (2017). Why optional stopping is a problem for Bayesians.
arXiv:1708.08278 [Math, Stat]. https://arxiv.org/abs/1708.08278</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Francis, G. (2016).
Equivalent statistics and data interpretation. Behavior Research Methods, 1–15.
https://doi.org/10.3758/s13428-016-0812-3</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Jeffreys, H. (1939).
Theory of probability (1st ed). Oxford University Press.</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Kelter, R. (2021).
Analysis of type I and II error rates of Bayesian and frequentist parametric
and nonparametric two-sample hypothesis tests under preliminary assessment of
normality. Computational Statistics, 36(2), 1263–1288.
https://doi.org/10.1007/s00180-020-01034-7</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Rouder, J. N. (2014).
Optional stopping: No problem for Bayesians. Psychonomic Bulletin & Review,
21(2), 301–308.</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Rouder, J. N.,
Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t
tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin
& Review, 16(2), 225–237. https://doi.org/10.3758/PBR.16.2.225</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">van de Schoot, R.,
Winter, S. D., Ryan, O., Zondervan-Zwijnenburg, M., & Depaoli, S. (2017). A
systematic review of Bayesian articles in psychology: The last 25 years.
Psychological Methods, 22(2), 217–239. https://doi.org/10.1037/met0000100</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Wagenmakers, E.-J.,
Wetzels, R., Borsboom, D., & van der Maas, H. L. J. (2011). Why
psychologists must change the way they analyze their data: The case of psi:
Comment on Bem (2011). Journal of Personality and Social Psychology, 100(3),
426–432. https://doi.org/10.1037/a0022790</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Wong, T. K., Kiers,
H., & Tendeiro, J. (2022). On the Potential Mismatch Between the Function
of the Bayes Factor and Researchers’ Expectations. Collabra: Psychology, 8(1),
36357. https://doi.org/10.1525/collabra.36357</span></p>
<p></p>Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com1tag:blogger.com,1999:blog-987850932434001559.post-71226961489069448602023-02-20T16:28:00.000+01:002023-02-20T16:28:42.935+01:00New Podcast: Nullius in VerbaTogether with my co-host Smriti Mehta we've started a new podcast: Nullius in Verba. It's a podcast about science - what it is, and what it could be. The introduction episode is up now, and new episodes will be released every other week starting this friday!<br /><br /> You can subscribe using clicking the links below: <br /><br />Apple Podcast: <p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><a href="https://podcasts.apple.com/us/podcast/nullius-in-verba/id1672861665" style="text-decoration: none;"><span style="-webkit-text-decoration-skip: none; background-color: transparent; color: #1155cc; font-family: Arial; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration-skip-ink: none; text-decoration: underline; vertical-align: baseline; white-space: pre-wrap; white-space: pre;">https://podcasts.apple.com/us/podcast/nullius-in-verba/id1672861665</span></a></p>Spotify:<p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><a href="https://open.spotify.com/show/0tw0yVZxuf2X4iN6kFay5A" style="text-decoration: none;"><span style="-webkit-text-decoration-skip: none; background-color: transparent; color: #1155cc; font-family: Arial; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration-skip-ink: none; text-decoration: underline; vertical-align: baseline; white-space: pre-wrap; white-space: pre;">https://open.spotify.com/show/0tw0yVZxuf2X4iN6kFay5A</span></a><span style="background-color: transparent; color: black; font-family: Arial; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre-wrap; white-space: pre;"> </span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">or by adding this RSS feed to your podcast player:</p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><a href="https://feed.podbean.com/nulliusinverba/feed.xml" style="text-decoration: none;"><span style="-webkit-text-decoration-skip: none; background-color: transparent; color: #1155cc; font-family: Arial; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration-skip-ink: none; text-decoration: underline; vertical-align: baseline; white-space: pre-wrap; white-space: pre;">https://feed.podbean.com/nulliusinverba/feed.xml</span></a></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="-webkit-text-decoration-skip: none; background-color: transparent; color: #1155cc; font-family: Arial; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration-skip-ink: none; text-decoration: underline; vertical-align: baseline; white-space: pre-wrap; white-space: pre;"> </span></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;"><span style="-webkit-text-decoration-skip: none; background-color: transparent; color: #1155cc; font-family: Arial; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration-skip-ink: none; text-decoration: underline; vertical-align: baseline; white-space: pre-wrap; white-space: pre;"></span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEieArJ6SoPdhkgu4aYN-Hb4iIXVgN9Nwl_KIWbUynyNFN10_WeDNpJAfwR-TR9OyjYxg0K8BcVE0vT6gpt0kZQpf2buepk7zWdnN_3mrWE5VexpHbL3fUW9ozhmyUz76YAwikub0vDmxAJxy5TPzsqMSDN1mbyLiJVIhGbCKz-wBA0iNdSbUiGdqXiD/s1563/nullius_in_verba_logo_smaller_size.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1563" data-original-width="1563" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEieArJ6SoPdhkgu4aYN-Hb4iIXVgN9Nwl_KIWbUynyNFN10_WeDNpJAfwR-TR9OyjYxg0K8BcVE0vT6gpt0kZQpf2buepk7zWdnN_3mrWE5VexpHbL3fUW9ozhmyUz76YAwikub0vDmxAJxy5TPzsqMSDN1mbyLiJVIhGbCKz-wBA0iNdSbUiGdqXiD/s320/nullius_in_verba_logo_smaller_size.jpg" width="320" /></a></div><br /> <p></p><p dir="ltr" style="line-height: 1.38; margin-bottom: 0pt; margin-top: 0pt;">We will release episodes on friday every other week (starting this friday). Topics in the first episodes are inspired by aphorisms in Francis Bacon's 'Novum Organum' (the galleon in our logo comes from the title page of his 1620 book, where it is passing between the mythical Pillars of Hercules
that stand either side of the Strait of Gibraltar, which have been
smashed through by Iberian sailors, opening a new world for exploration
and marking the exit from the well-charted waters of the Mediterranean
into the Atlantic Ocean. Bacon hoped that empirical investigation will
similarly smash the old scientific ideas and lead to greater
understanding of nature and the world.
<br />
<br />As we explain in the introduction, the title of the podcast comes from the motto of the Royal Society. <br /><br />Our logo is set
in typeface Kepler by Robert Slimbach. Our theme song is Newton’s Cradle
by Grandbrothers. You see we are going all in on subtle science references. <br /><br />We hope you'll enjoy listening along as we discuss themes like confirmation bias, skepticism, eminence, the 'itch to publish' and in the first episode, the motivations to do science. <br /><span style="-webkit-text-decoration-skip: none; background-color: transparent; color: #1155cc; font-family: Arial; font-size: 11pt; font-style: normal; font-variant: normal; font-weight: 400; text-decoration-skip-ink: none; text-decoration: underline; vertical-align: baseline; white-space: pre-wrap; white-space: pre;"></span></p>Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com0tag:blogger.com,1999:blog-987850932434001559.post-60541955275735416672022-07-18T08:28:00.003+02:002022-07-19T07:16:18.864+02:00 Irwin Bross justifying the 0.05 alpha level as a means of communication between scientists<p><span lang="EN-US" style="mso-ansi-language: EN-US;">This blog
post is based on the chapter “Critical Levels, Statistical Language, and
Scientific Inference” by Irwin D. J. Bross </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(1971) in the proceedings of the symposium on the foundations of statistical inference held in 1970</span><span lang="EN-US" style="mso-ansi-language: EN-US;">. Because the conference proceedings
might be difficult to access, I am citing extensively from the original source.
<a href="https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(04)17168-7/fulltext">Irwin
D. J. Bross</a> [1921-2004] was a biostatistician at Roswell Park Cancer
Institute in Buffalo up to 1983. </span>
</p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHc0guCMZ8n6RahhcppPa8KFeHxMDLbBznJeFu3XxyURX5tiRc-H3ABNscjSg8q8hIGxxVWDkJC8N1gkDZn86XkWAxD1EKluvkgVpIopZxzxvNB2ZQcKzdU02vQJaXMINOBpelq9CY7FQhNTHnx3IioYBxBmJtp3TuIEssLmxd0nxIWE64yAjPB-KO/s388/bross.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="388" data-original-width="267" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhHc0guCMZ8n6RahhcppPa8KFeHxMDLbBznJeFu3XxyURX5tiRc-H3ABNscjSg8q8hIGxxVWDkJC8N1gkDZn86XkWAxD1EKluvkgVpIopZxzxvNB2ZQcKzdU02vQJaXMINOBpelq9CY7FQhNTHnx3IioYBxBmJtp3TuIEssLmxd0nxIWE64yAjPB-KO/s320/bross.jpg" width="220" /></a></div><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"><i>Irwin D. J. Bross</i></span></p><p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"><i> </i> <br /></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Criticizing
the use of thresholds such as an alpha level of 0.05 to make dichotomous
inferences is of all times. Bross writes in 1981: “<i>Of late the attacks on
critical levels (and the statistical methods based on these levels) have become
more frequent and more vehement</i>.” I feel the same way, but it seems
unlikely the vehemence of criticisms has been increasing for half a century. A
more likely explanation is perhaps that some people, like Bross and myself,
become increasingly annoyed by such criticisms.</span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Bross reflects
on how very few justifications of the use of alpha levels exists in the
literature, because “<i>Elementary statistics texts are not equipped to go into
the matter; advanced texts are too preoccupied with the latest and fanciest
statistical techniques to have space for anything so elementary. Thus the
justifications</i></span><i><span lang="EN-US" style="color: #616161; font-family: "Times New Roman",serif; font-size: 10pt; mso-ansi-language: EN-US;"> </span></i><i><span lang="EN-US" style="mso-ansi-language: EN-US;">for critical levels that are
commonly offered are flimsy, superficial, and badly outdated.</span></i><span lang="EN-US" style="mso-ansi-language: EN-US;">” He notes how the use of critical
values emerged in a time where statisticians had more practical experience, but
that “<i>Unfortunately, many of the theorists nowadays have lost touch with
statistical practice and as a consequence, their work is mathematically
sophisticated but scientifically very naive.</i>” </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Bross sets
out to consider which justification can be given for the use of critical alpha
levels. He would like such a justification to convince those who use
statistical methods in their research, and statisticians who are familiar with
statistical practice. He argues that the purpose of a medical researcher “<i>is
to reach scientific conclusions concerning the relative efficacy (or safety) of
the drugs under test. From the investigator's standpoint, he would like to make
statements which are reliable and informative. From a somewhat broader standpoint,
we can consider the larger communication network that exists in a given research
area - the network which would connect a clinical pharmacologist with his colleagues
and with the practicing physicians who might make use of his findings. <b>Any realistic
picture of scientific inference must take some account of the communication
networks that exist in the sciences</b>.” </i></span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">It is rare
to see biostatisticians explicitly embrace the pragmatic and social aspect of
scientific inference. He highlights three main points about communication
networks. “<i>First, messages generate messages.</i>” Colleagues might
replicate a study, or build on it, or apply the knowledge. “<i>A second key
point is: discordant messages produce noise in the network</i>”. “<i>A third
key point is: statistical methods are useful in controlling the noise in the
network</i>”. The critical level set by researchers controls the noise in the
network. Too much noise in a network impedes scientific progress, because
communication breaks down. He writes “<i>Thus the specification of the critical
levels […] has proved </i>in practice<i> to be an effective method for controlling
the noise in communication networks.</i>” Bross also notes that the critical alpha
level in itself is not enough to reduce noise – it is just one component of a
well-designed experiment that reduces noise in the network. Setting a
sufficiently low alpha level is therefore one aspect that contributes to a
system where people in the network can place some reliance on claims that are
made because noise levels are not too high. </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><i><span lang="EN-US" style="mso-ansi-language: EN-US;">“This
simple example serves to bring out several features of the usual critical level
techniques which are often overlooked although they are important in practice.
Clearly, if each investigator holds the proportion of false positive reports in
his studies at under 5%, then the proportion of false positive reports from all
of the studies carried out by the participating members of the network will be
held at less than 5%. This property does not sound very impressive – it sounds
like the sort of property one would expect any sensible statistical method to
have. But it might be noted that most of the methods advocated by the
theoreticians who object to critical levels lack this and other important properties
which facilitate control of the noise level in the network.”</span></i></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">This point,
common among all error statisticians, has repeatedly raised its head in
response to suggestions to abandon statistical significance, or to stop
interpreting <i>p</i>-values dichotomously. Of course, one can choose not to
care about the rate at which researchers make erroneous claims, but it is
important to realize the consequences of not caring about error rates. Of
course, one can work towards a science where scientists no longer make claims,
but generate knowledge through some other mechanism. But recent proposals to
treat <i>p</i>-values as continuous measures of evidence </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Muff et
al., 2021; but see Lakens, 2022a)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> or to use estimation instead of
hypothesis tests </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Elkins
et al., 2021; but see Lakens, 2022b)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> do not outline what such an
alternative mode of knowledge generation would look like, or how researchers
will be prevented from making claims about the presence or absence of effects. </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Bross proposes
an intriguing view on statistical inference where “</span><i><span style="mso-ansi-language: #1000;">statistics is used as a means</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">of communication between the members of the network.</span></i><span lang="EN-US" style="mso-ansi-language: EN-US;">” He views statistics not as a way
to learn whether idealized probability distributions accurately reflect the
empirical reality in infinitely repeated samples, but as a way to communicate
assertions that are limited by the facts. He argues that specific ways of
communicating only become widely used if they are effective. Here, I believe he
fails to acknowledge that ineffective communication systems can also evolve,
and it is possible that scientists en masse use techniques, not because they
are efficient ways of communicating facts, but because they will lead to
scientific publications. The idea that statistical inferences are a ‘mindless
ritual’ has been proposed </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Gigerenzer,
2018)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">, and there is no doubt that many
scientists simply imitate the practices they see. Furthermore, the replication
crisis has shown that huge error rates in subfields of the scientific literature
can exists for decades. The problems associated with these large error rates (e.g.,
failures to replicate findings, inaccurate effect size estimate) can sometimes
only very slowly lead to a change in practice. So, arguing a practice survives because
it works is risky. Whether current research practices are effective - or
whether other practices would be more effective – requires empirical evidence.
Randomized controlled trials seem a bridge to far to compare statistical
approaches, but natural experiments by journals that abandon p-values support
Bross’s argument to some extent. When the journal of Basic and Applied
Psychology abandoned <i>p</i>-values, the consequence was that researchers
claimed effects were present at a higher error rate than if claims had been
limited by typical alpha level thresholds </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Fricker
et al., 2019)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">.</span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Whether
efficient or not, statistical language surrounding critical thresholds is in
widespread use. Bross discusses how an alpha level of 5% or 1% is a convention.
Like many linguistic conventions, the threshold of 5% is <i>somewhat arbitrary</i>,
and reflects the influence of statisticians like Karl Pearson and Ronald Fisher.
However, the threshold is not <i>completely arbitrary</i>. Bross asks us to
imagine what would have happened had the alpha level of 0.001 been proposed, or
an alpha level of 0.20. In both cases, he believes the convention would not
have spread – in the first case because in many fields there are not sufficient
resources to make claims at such a low error rate, and in the second case because
few researchers would have found that alpha level a satisfactory quantification
of ‘rare’ events. He, I think correctly, observes this means that the alpha
level of 0.05 is not completely arbitrary, but that it reflects a
quantification of ‘rare’ that researchers believe has sufficient practical
value to be used in communication. Bross argues that the convention of a 5%
alpha level spread because it sufficiently matched with what most scientists
considered as an appropriate probability level to define ‘rare’ events, for
example as when Fisher </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(1956)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> writes “Either an exceptionally
rare chance has occurred, or the theory of random distribution is not true.” </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Of course,
one might follow Bross and ask “</span><i><span style="mso-ansi-language: #1000;">But
is there any reason to single out one</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">particular value,
5%, instead of some other value such as 3.98% or 7.13%</span></i><i><span lang="EN-US" style="mso-ansi-language: EN-US;">?”</span></i><span lang="EN-US" style="mso-ansi-language: EN-US;">. He writes: “</span><i><span style="mso-ansi-language: #1000;">Again, inconformity with the linguistic</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">patterns in setting conventions it is natural to use a </span></i><span style="mso-ansi-language: #1000;">round number <i>like 5%. Such</i></span><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">round numbers serve to avoid any suggestion that the critical value has
been</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">gerrymandered or otherwise picked to prove a
point in a particular study.</span></i><i><span style="mso-ansi-language: EN-US;">
</span></i><i><span style="mso-ansi-language: #1000;">From an abstract
standpoint, it might seem more logical to allow a range of</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">critical values rather than to choose one number but to do so would be
to</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">go contrary to the linguistic habits of
fact-limited languages. Such languages</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">tend to minimize
the freedom of choice of the speaker in order to insure that</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">a statement results from factual evidence and not from a little
language-game</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">played by the speaker.</span></i><span lang="EN-US" style="mso-ansi-language: EN-US;">” </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">In a
recent paper we made a similar point </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial; mso-bidi-font-size: 12.0pt;">(Uygun Tunç et al., 2021)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">: “The conventional use of an alpha
level of 0.05 can also be explained by the requirement in methodological
falsificationism that statistical decision procedures are specified before the
data is analyzed (see Popper, 2002, sections 19-20; Lakatos, 1978, p. 23-28).
If researchers are allowed to set the alpha level after looking at the data
there is a possibility that confirmation bias (or more intentional
falsification-deflecting strategies) influences the choice of an alpha level.
An additional reason for a conventional alpha level of 0.05 is that before the
rise of the internet it was difficult to transparently communicate the
pre-specified alpha level for any individual test to peers. The use of a
default alpha level therefore effectively functioned as a pre-specification.
For a convention to work as a universal pre-specification, it must be accepted
by nearly everyone, and be extremely resistant to change. If more than a single
conventional alpha level exists, this introduces the risk that confirmation
bias influences the choice of an alpha level.” Our thoughts seem to be very
much aligned with those of Bross. </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Bross
continues and writes “</span><i><span style="mso-ansi-language: #1000;">Anyone
familiar with certain areas of the scientific literature will be well</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">aware of the need for curtailing language-games. Thus if there were no
5% level</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">firmly established, then some persons would
stretch the level to 6% or 7% to</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">prove their point.
Soon others would be stretching to 10% and 15% and the</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">jargon would become meaningless. Whereas nowadays a phrase such as</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><span style="mso-ansi-language: #1000;">statistically significant difference<i> provides some assurance that the
results</i></span><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">are not merely a manifestation of sampling
variation, the phrase would mean</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">very little if
everyone played language-games. To be sure, there are always a</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">few folks who fiddle with significance levels-who will switch from
two-tailed</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">to one-tailed tests or from one significance
test to another in an effort to get</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">positive results.
However such gamesmanship is severely frowned upon and is</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">rarely practiced by persons who are </span></i><span style="mso-ansi-language: #1000;">native speakers<i> of fact-limited scientific</i></span><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">languages</span></i><i><span lang="EN-US" style="mso-ansi-language: EN-US;">
- </span></i><i><span style="mso-ansi-language: #1000;">it is the mark of an
amateur.</span></i><span lang="EN-US" style="mso-ansi-language: EN-US;">” </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">We
struggled with the idea that changing the alpha level (and especially
increasing the alpha level) might confuse readers when ‘statistically
significant’ no longer means ‘rejected with a 5% alpha level) in our recent
paper on justifying alpha levels </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Maier
& Lakens, 2022)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">. We wrote “<i>Finally, the use of a
high alpha level might be missed if readers skim an article. We believe this
can be avoided by having each scientific claim accompanied by the alpha level
under which it was made. Scientists should be required to report their alpha levels
prominently, usually in the abstract of an article alongside a summary of the
main claim.</i>” It might in general be an improvement if people write ‘we
reject an effect size of X at an alpha level of 5%’, but this is especially
true of researchers choose to deviate from the conventional 5% alpha level. </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">I like how
Bross has an extremely pragmatic but still principled view on statistical
inferences. He writes: “</span><i><span style="mso-ansi-language: #1000;">This
means that we have to abandon the traditional </span></i><span style="mso-ansi-language: #1000;">prescriptive<i> attitude and adopt the </i>descriptive<i>
approach which is characteristic of the empirical</i></span><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">sciences. If we do so, then we get a very different picture of what
statistica</span></i><i><span lang="EN-US" style="mso-ansi-language: EN-US;">l </span></i><i><span style="mso-ansi-language: #1000;">and scientific inference is all about. It is
very difficult, I believe, to get such</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">a picture unless
you have had some first hand experience as an independent</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">investigator in a scientific study. You then learn that drawing
conclusions</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">from statistical data can be a traumatic
experience. A statistical consultant</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">who takes a
detached view of</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">things</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">just</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">does not feel this pain.</span></i><span lang="EN-US" style="mso-ansi-language: EN-US;">” This point is made by other applied statisticians, and it is a really
important one. There are consequences of statistical inferences that you only
experience when you spend several years trying to answer a substantive research
question. Without that experience, it is difficult to give practical
recommendations about what researchers should want when using statistics. </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">He
continues “</span><i><span style="mso-ansi-language: #1000;">What you want</span></i><i><span lang="EN-US" style="mso-ansi-language: EN-US;"> - </span></i><i><span style="mso-ansi-language: #1000;">and want desperately</span></i><i><span lang="EN-US" style="mso-ansi-language: EN-US;"> - </span></i><i><span style="mso-ansi-language: #1000;">is all the protection you can get</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">against the "slings and arrows of outrageous fortune". You
want to say something</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">informative and useful about the origins and
nature of a disease or health</span></i><i><span style="mso-ansi-language: EN-US;">
</span></i><i><span style="mso-ansi-language: #1000;">hazard. But you do not want
your statement to come back and haunt you for</span></i><i><span style="mso-ansi-language: EN-US;"> </span></i><i><span style="mso-ansi-language: #1000;">the rest of your life.</span></i><span lang="EN-US" style="mso-ansi-language: EN-US;">” Of course, Bross should have said ‘One thing you might want’ (because
now his statement is just another example of The Statistician’s Fallacy </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Lakens,
2021)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">). But with this small amendment, I
think there are quite some scientists who want this from their statistical inferences.
He writes “</span><span style="mso-ansi-language: #1000;">When you announce a new
finding, you put your scientific</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">reputation on the line. You colleagues probably
cannot remember all your</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">achievements, but they will never forget any of
your mistakes! Second</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #1000;">thoughts like these produce an acute sense of
insecurity.</span><span lang="EN-US" style="mso-ansi-language: EN-US;">” Not all
scientists might feel like this, I hope we are willing to forget some mistakes
people make, and I fear the consequences of making too many incorrect claims on
the reputation of a researcher is not as severe as Bross suggests<a href="#_edn1" name="_ednref1" style="mso-endnote-id: edn1;" title=""><span class="MsoEndnoteReference"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="EN-US" style="font-size: 11pt; line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[i]</span></span></span></span></a>.
But I think many fellow researchers will experience some fear their findings do
not hold up (at least until they have been replicated several times), and that
hearing researchers failed to replicate a finding yields some negative affect.</span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;">I think
Bross hits the nail on the head when it comes to thinking about justifications
of the use of alpha levels as thresholds to make claims. The justification for
this practice is social and pragmatic in nature, not statistical (cf. </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial; mso-bidi-font-size: 12.0pt;">Uygun Tunç et al., 2021)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">. If we want to evaluate if current
practices are useful or not, we have to abandon a prescriptive approach, and
rely on a descriptive approach (Bross, p. 511). Anyone proposing an alternative
to the use of alpha levels should not make prescriptive arguments, but provide descriptive
data (or at least predictions) that highlight how their preferred approach to
statistical inferences will improve communication between scientists. </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">References
</span></b></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 8pt; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Bross, I.
D. (1971). Critical levels, statistical language and scientific inference. In <i>Foundations
of statistical inference</i> (pp. 500–513). Holt, Rinehart and Winston.</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Elkins, M. R.,
Pinto, R. Z., Verhagen, A., Grygorowicz, M., Söderlund, A., Guemann, M.,
Gómez-Conesa, A., Blanton, S., Brismée, J.-M., Ardern, C., Agarwal, S., Jette,
A., Karstens, S., Harms, M., Verheyden, G., & Sheikh, U. (2021).
Statistical inference through estimation: Recommendations from the
International Society of Physiotherapy Journal Editors. <i>Journal of
Physiotherapy</i>. https://doi.org/10.1016/j.jphys.2021.12.001</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Fisher, R. A.
(1956). <i>Statistical methods and scientific inference</i> (Vol. viii). Hafner
Publishing Co.</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Fricker, R. D.,
Burke, K., Han, X., & Woodall, W. H. (2019). Assessing the Statistical
Analyses Used in Basic and Applied Social Psychology After Their p-Value Ban. <i>The
American Statistician</i>, <i>73</i>(sup1), 374–384.
https://doi.org/10.1080/00031305.2018.1537892</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Gigerenzer, G.
(2018). Statistical Rituals: The Replication Delusion and How We Got There. <i>Advances
in Methods and Practices in Psychological Science</i>, <i>1</i>(2), 198–218.
https://doi.org/10.1177/2515245918771329</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Lakens, D. (2021).
The practical alternative to the p value is the correctly used p value. <i>Perspectives
on Psychological Science</i>, <i>16</i>(3), 639–648.
https://doi.org/10.1177/1745691620958012</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Lakens, D. (2022a).
Why P values are not measures of evidence. <i>Trends in Ecology & Evolution</i>.
https://doi.org/10.1016/j.tree.2021.12.006</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Lakens, D. (2022b).
Correspondence: Reward, but do not yet require, interval hypothesis tests. <i>Journal
of Physiotherapy</i>, <i>68</i>(3), 213–214.
https://doi.org/10.1016/j.jphys.2022.06.004</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Maier, M., &
Lakens, D. (2022). Justify Your Alpha: A Primer on Two Practical Approaches. <i>Advances
in Methods and Practices in Psychological Science</i>, <i>5</i>(2),
25152459221080396. https://doi.org/10.1177/25152459221080396</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Muff, S., Nilsen, E.
B., O’Hara, R. B., & Nater, C. R. (2021). Rewriting results sections in the
language of evidence. <i>Trends in Ecology & Evolution</i>.
https://doi.org/10.1016/j.tree.2021.10.009</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Uygun Tunç, D.,
Tunç, M. N., & Lakens, D. (2021). <i>The Epistemic and Pragmatic Function
of Dichotomous Claims Based on Statistical Hypothesis Tests</i>. </span><span lang="NL" style="mso-bidi-font-family: Arial;">PsyArXiv.
https://doi.org/10.31234/osf.io/af9by</span></p>
<p class="MsoNormal" style="line-height: normal; margin-bottom: 0cm; margin-top: 0cm; mso-add-space: auto; mso-margin-bottom-alt: 8.0pt; mso-margin-top-alt: 0cm;"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<div style="mso-element: endnote-list;"><br clear="all" />
<hr align="left" size="1" width="33%" />
<div id="edn1" style="mso-element: endnote;">
<p class="MsoEndnoteText"><a href="#_ednref1" name="_edn1" style="mso-endnote-id: edn1;" title=""><span class="MsoEndnoteReference"><span lang="NL"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="NL" style="font-size: 10pt; line-height: 107%; mso-ansi-language: NL; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[i]</span></span></span></span></span></a><span lang="NL" style="mso-ansi-language: EN-US;"> </span><span lang="EN-US" style="mso-ansi-language: EN-US;">I recently saw the bio of a social psychologist
who has produces a depressingly large number of incorrect claims in the
literature. His bio made no mention of this fact, but proudly boasted about the
thousands of times he was cited, even though most citations were for work that
did not survive the replication crisis. How much reputations should suffer is
an intriguing question that I think too many scientists will never feel comfortable
addressing. </span></p>
</div>
</div>
<p></p>Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com0tag:blogger.com,1999:blog-987850932434001559.post-9063529100356025742022-05-09T22:12:00.004+02:002022-05-10T09:02:20.460+02:00Tukey on Decisions and Conclusions<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In 1955
Tukey gave a dinner talk about the difference between decisions and conclusions
at a meeting of the Section of Physical and Engineering Science of the American
Statistical Association. The talk was published in 1960. The distinction relates
directly to different goals researchers might have when they collect data. This
blog is largely a summary of <a href="https://www.tandfonline.com/doi/abs/10.1080/00401706.1960.10489909">his
paper</a>. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p><div class="separator" style="clear: both; text-align: center;"></div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7FzDLRxxKhfyqBawDpgz3jALHwtx1eAqDz4Q35zoAUUqYg5FxLW0yBJS6twojFO_lhL47r22DSm5d3fIm6lVA3BA69QdJOjPMMPVo-doBipsFtsM65jNeA0Ikun3ptTqRgrxcjGruxJBy8aEO76IvXauRF4L4C35hGg1lei5Y-vQqTODQOil89xG0/s324/John_Wilder_Tukey.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="324" data-original-width="234" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg7FzDLRxxKhfyqBawDpgz3jALHwtx1eAqDz4Q35zoAUUqYg5FxLW0yBJS6twojFO_lhL47r22DSm5d3fIm6lVA3BA69QdJOjPMMPVo-doBipsFtsM65jNeA0Ikun3ptTqRgrxcjGruxJBy8aEO76IvXauRF4L4C35hGg1lei5Y-vQqTODQOil89xG0/s320/John_Wilder_Tukey.png" width="231" /></a></div><br /><p></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Tukey was
concerned about the ‘tendency of decision theory to attempt to conquest all of
statistics’. In hindsight, he needn’t have worried. In the social sciences,
most statistics textbooks do not even discuss decision theory. His goal was to
distinguish decisions from conclusions, to carve out a space for ‘conclusion
theory’ to complement decision theory. He distinguishes decisions from conclusions.
</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In
practice, making a decision means to ‘decide to act for the present as if’.
Possible actions are defined, possible states of nature identified, and we make
an inference about each state of nature. Decisions can be made even when we
remain extremely uncertain about any ‘truth’. Indeed, in extreme cases we can
even make decisions without access to any data. We might even decide to act as
if two mutually exclusive states of nature are true! For example, we might buy
a train ticket for a holiday three months from now, but also take out life
insurance in case we die tomorrow. <span style="mso-spacerun: yes;"> </span><span style="mso-spacerun: yes;"> </span></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Conclusions
differ from decisions. First, conclusions are established without taking
consequences into consideration. Second, conclusions are used to build up a ‘fairly
well-established body of knowledge’. As Tukey writes: “A conclusion is a
statement which is to be accepted as applicable to the conditions of an
experiment or observation unless and until unusually strong evidence to the
contrary arises.” A conclusion is not a decision on how to act in the present.
Conclusions are to be <i>accepted</i>, and thereby incorporated into what Frick
(1996) calls a ‘corpus of findings’. According to Tukey, conclusions are used
to narrow down the number of working hypotheses still considered consistent
with observations. Conclusions should be reached, not based on their
consequences, but because of their lasting (but not everlasting, as conclusions
can now and then be overturned by new evidence) contribution to scientific
knowledge. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">Tests of
hypotheses</span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">According
to Tukey, a test of hypotheses can have two functions. The first function is as
a decision procedure, and the second function is to reach a conclusion. In a
decision procedure the goal is to choose a course of action given an acceptable
risk. This risk can be high. For example, a researcher might decide not to
pursue a research idea after a first study, designed to have 80% power for a
smallest effect size of interest, yields a non-significant result. The error
rate is at most 20%, but the researcher might have enough good research ideas
to not care. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The second
function is to reach a conclusion. This is done, according to Tukey, by
controlling the Type 1 and Type 2 error rate at ‘suitably low levels’ (Note:
Tukey’s discussion of concluding an effect is absent is hindered somewhat by
the fact that equivalence tests were not yet widely established in 1955 – Hodges
& Lehman’s paper appeared in 1954). Low error rates, such as the conventions
to use a 5% of 1% alpha level, are needed to draw conclusions that can enter
the corpus of findings (even though some of these conclusions will turn out to
be wrong, in the long run). </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">Why
would we need conclusions? </span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">One might
reasonably wonder if we need conclusions in science. Tukey also ponders this
question in Appendix 2. He writes “Science, in the broadest sense, is both one
of the most successful of human affairs, and one of the most decentralized. In
principle, each of us puts his evidence (his observations, experimental or not,
and their discussion) before all the others, and in due course an adequate
consensus of opinion develops.” He argues not for an epistemological reason,
nor for a statistical reason, but for a sociological reason. Tukey writes: </span><span lang="EN-US" style="mso-ansi-language: EN-US;"><span lang="EN-US" style="mso-ansi-language: EN-US;">“</span>There
are four types of difficulty, then, ranging from communication through
assessment to mathematical treatment, each of which by itself will be
sufficient, for a long time, to prevent the replacement, in science, of the
system of conclusions by a system based more closely on today’s decision
theory.” He notes how scientists can no longer get together in a single room (as
was somewhat possible in the early decades of the Royal Society of London) to
reach consensus about decisions. Therefore, they need to communicate conclusions,
as “In order to replace conclusions as the basic means of communication, it
would be necessary to rearrange and replan the entire fabric of science.” </span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">I
hadn’t read Tukey’s paper when we wrote our preprint “<a href="https://psyarxiv.com/af9by/">The Epistemic and Pragmatic Function of
Dichotomous Claims Based on Statistical Hypothesis Tests</a>”. In this
preprint, we also discuss a sociological reason for the presence of dichotomous
claims in science. We also ask: “Would it be possible to organize science in a
way that relies less on tests of competing theories to arrive at
intersubjectively established facts about phenomena?” and similarly conclude: “Such
alternative approaches seem feasible if stakeholders agree on the research
questions that need to be investigated, and methods to be utilized, and
coordinate their research efforts”. <span style="mso-spacerun: yes;">We should add a citation to Tukey's 1960 paper. <br /></span></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">Is the
goal of an study a conclusion, a decision, or both? </span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Tukey writes
he “looks forward to the day when the history and status of tests of hypotheses
will have been disentangled.” I think that in 2022 that day has not yet come. At
the same time, Tukey admits in Appendix 1 that the two are sometimes intertwined.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">A situation
Tukey does not discuss, but that I think is especially difficult to
disentangle, is a cumulative line of research. Although I would prefer to only
build on an established corpus of findings, this is simply not possible. Not
all conclusions in the current literature are reached with low error rates.
This is true both for claims about the absence of an effect (which are rarely
based on an equivalence test against a smallest effect size of interest with a
low error rate), as for claims about the presence of an effect, not just
because of p-hacking, but also because I might want to build on an exploratory
finding from a previous study. In such cases, I would like to be able to <i>conclude</i>
the effects I build on are established findings, but more often than not, I
have to <i>decide</i> these effects are worth building on. The same holds for
choices about the design of a set of studies in a research line. I might decide
to include a factor in a subsequent study, or drop it. These decisions are
based on conclusions with low error rates if I had the resources to collect
large samples and perform replication studies, but other times they involve
decisions about how to act in my next study with quite considerable risk. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">We allow
researchers to publish feasibility studies, pilot studies, and exploratory
studies. We don’t require every study to be a Registered Report of Phase 3
trial. Not all information in the literature that we build on has been
established with the rigor Tukey associates with conclusions. And the
replication crisis has taught us that more conclusions from the past are later
rejected than we might have thought based on the alpha levels reported in the
original articles. And in some research areas, where data is scarce, we might
need to accept that, if we want to learn anything, the conclusions will always
more tentative (and the error rates accepted in individual studies will be higher)
than in research areas where data is abundant.</span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Even if decisions and conclusions can not be completely disentangled, reflecting on their relative differences is very useful, as I think it can help us to clarify the goal we have when we collect data. <br /></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><i><span lang="EN-US" style="mso-ansi-language: EN-US;">For a
2013 blog post by Justin Esarey, who found the distinction a bit less useful
than I found it, see <a href="https://polmeth.org/blog/scientific-conclusions-versus-scientific-decisions-or-we%E2%80%99re-having-tukey-thanksgiving">https://polmeth.org/blog/scientific-conclusions-versus-scientific-decisions-or-we%E2%80%99re-having-tukey-thanksgiving</a>
</span></i></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">References</span><span style="mso-ansi-language: #1000;"></span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Frick, R. W. (1996).
The appropriate use of null hypothesis testing. <i>Psychological Methods</i>, <i>1</i>(4),
379–390. <a href="https://doi.org/10.1037/1082-989X.1.4.379">https://doi.org/10.1037/1082-989X.1.4.379</a></span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Tukey, J. W. (1960).
Conclusions vs decisions. <i>Technometrics</i>, <i>2</i>(4), 423–433.</span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Uygun Tunç, D., Tunç,
M. N., & Lakens, D. (2021). <i>The Epistemic and Pragmatic Function of
Dichotomous Claims Based on Statistical Hypothesis Tests</i>. PsyArXiv. <a href="https://doi.org/10.31234/osf.io/af9by">https://doi.org/10.31234/osf.io/af9by</a></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com0tag:blogger.com,1999:blog-987850932434001559.post-38878686521622718892022-05-03T11:38:00.004+02:002022-05-03T17:09:53.342+02:00Collaborative Author Involved Replication Studies<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Recently a
new category of studies have started to appear in the psychological literature that
provide the strongest support to date for a replication crisis in psychology: Large
scale collaborative replication studies where the authors of the original study
are directly involved in the study. These replication studies have often provided
conclusive demonstrations of the absence of any effect large enough to matter. Despite
considerable attention for these extremely interesting projects, I don’t think
the scientific community has fully appreciated what we have learned from these
studies. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">Three examples
of Collaborative Author Involved Replication Studies</span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Vohs and
colleagues (2021) performed a multi-lab replication study of the ego-depletion
effect, which (deservedly) has become a poster child of non-replicable effects
in psychology. The teams used different combinations of protocols, allowing an
unsuccessful prediction to generalize across minor variations in how the
experiment was operationalized. Across these conditions, a non-significant effect
was observed of d = 0.06, 95%CI[-0.02;0.14]. Although the authors regrettably did
not specify a smallest effect size of interest in their frequentist analyses, they
mention “we pitted a point-null hypothesis, which states that the effect is
absent, against an informed one-sided alternative hypothesis centered on a
depletion effect (δ) of 0.30 with a standard deviation of 0.15” in their
Bayesian analyses. Based on the confidence interval, we can reject effects of d
= 0.3, and even d = 0.2, suggesting that we have extremely informative data
concerning the absence of an effect most ego-depletion researchers would
consider is large enough to matter. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Morey et al
(2021) performed a multi-lab replication study of the Action-Sentence
Compatibility effect (Glenberg & Kaschak, 2002). I cited the original paper
in my PhD thesis, and it was an important finding that I built on, so I was
happy to join this project. As written in the replication study, the original team,
together with the original authors, “established and pre-registered ranges of
effects on RT that we would deem (a) uninteresting and inconsistent with the
ACE theory: less than 50 ms.” An effect between 50 ms and 100 ms was seen as
inconsistent with the previous literature, but in line with predictions of the
ACE effect. The replication study consisted (after exclusions) of 903 native
English speakers, and 375 non-native English speakers. The original study had used
44, 70, and 72 participants across 3 studies. The conclusion in the replication
study was that <span style="mso-spacerun: yes;"> </span>the median ACE
interactions were close to 0 and all within the range that we pre-specified as
negligible and inconsistent with the existing ACE literature. There was little
heterogeneity.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Last week,
Many Labs 4 was published (Klein et al., 2022). This study was designed to
examine the mortality salience effect (which I think deserve the same poster
child status of a non-replicable effect in psychology, but which seems to have
gotten less attention so far). Data from 1550 participants was collected across
17 labs, some which performed the study with involvement of the original
author, and some which did not. Several variations of the analyses were
preregistered, but none revealed the predicted effect, <i>Hedges’ g</i> = 0.07,
95% CI = [-0.03, 0.17] (for exclusion set 1). The authors did not provide a
formal sample size justification based on a smallest effect size of interest,
but in a sensitivity power analysis indicate they had 95% power for effect
sizes of d = 0.18 to d = 0.21. If we assume all authors found effect sizes
around d = 0.2 small enough to no longer support their predictions, we can see
based on the confidence intervals that we can indeed exclude effect sizes large
enough to matter. The mortality salience effect, even with involvement of the
original authors, seems to be too small to matter. There was little
heterogeneity in effect sizes (in part because the absence of an effect). </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">These are
just three examples (there are more, of which the multi-lab test of the facial
feedback hypothesis by Coles et al., 2022, is worth highlighting), but they
highlight some interesting properties of collaborative author involved
replication studies. I will highlight four strengths of these studies.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">Four
strengths of Collaborative Author Involved Replication Studies</span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">1) The
original authors are extensively involved in the design of the study. They sign
off on the final design, and agree that the study is, with the knowledge they
currently have, the best test of their prediction. This means the studies tell
us something about the<i> predictive validity</i> of state of the art knowledge
in a specific field. If the predictions these researchers make are not
corroborated, the knowledge we have accumulated in these research areas are is
not reliable enough to make successful predictions.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">2) The
studies are not always direct replications, but the best possible test of the
hypothesis, in the eyes of the researchers involved. Criticism on past
replication studies has been that directly replicating a study performed many
years ago is not always insightful, as the context has changed (even though
Many Labs 5 found no support for this criticism). In this new category of
collaborative author involved replication studies, the original authors are
free to design the best possible test of their prediction. If these tests fail,
we can not attribute the failure to replicate to the ‘protective belt’ of
auxiliary hypotheses that no longer hold. Of course, it is possible that the
theory can be adjusted in a constructive manner after this unsuccessful
prediction. But at this moment, these original authors do not have a solid
understanding of their research topic to be able to predict if an effect will
be observed. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">3) The other
researchers involved in these projects often have extensive expertise in the
content area. They are not just researchers interested in mechanistically
performing a replication study on a topic they have little expertise with.
Instead, many of the researchers consists of peers who have worked in a
specific research area, published on the topic of the replication study, but
have collectively developed some doubts about the reliability of past claims,
and have decided to spend some of their time replicating a previous finding. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">4) The
statistical analyses in these studies yield informative conclusions. The
studies typically do not conclude the prediction was unsuccessful based on <i>p</i>
> 0.05 in a small sample. In the most informative studies, original authors
have explicitly specified a smallest effect size of interest, which makes it
possible to perform an equivalence test, and statistically reject the presence
of any effect deemed large enough to matter. In other cases, Bayesian hypothesis
tests are performed which provide support for the null, compared to the
alternative, model. This makes these replications studies severe tests of the
predicted effect. In cases where original authors did not specify a smallest
effect size of interest, the very large sample sizes allow readers to examine
effects that can be rejected based on the observed confidence interval, and in
all the studies discussed here, we can reject the presence of effects large
enough to be considered meaningful. There is most likely not a PhD student in
the world who would be willing to examine these effects, given the size that
remains possible after these collaborative author involved replication studies.
We can never conclude an effect is exactly zero, but that hardly matters – the effects
are clearly too small to study. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">The
Steel Man for Replication Crisis Deniers</span></b></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Given the reward
structures in science, it is extremely rewarding for individual researchers to
speak out against the status quo. Currently, the status quo is that the
scientific community has accepted there is a replication crisis. Some people
attempt to criticize this belief. This is important. All established beliefs in
science should be open to criticism.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Most papers
that aim to challenge the fact that many scientific domains have a surprising
difficulty successfully replicating findings once believed reliable focus on
the 100 studies in the Replicability Project: Psychology that was started a
decade ago, and published in 2015. This project was incredibly successful in creating
awareness of concerns around replicability, but it was not incredibly
informative about how big the problem was. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In the
conclusion of the RP:P, the authors wrote: “<i>After this intensive effort to
reproduce a sample of published psychological findings, how many of the effects
have we established are true? Zero. And how many of the effects have we
established are false? Zero. Is this a limitation of the project design? No. It
is the reality of doing science, even if it is not appreciated in daily
practice. Humans desire certainty, and science infrequently provides it. As
much as we might wish it to be otherwise, a single study almost never provides
definitive resolution for or against an effect and its explanation.</i>” The
RP:P was an important project, but it is no longer the project to criticize if
you want to provide evidence against the presence of a replication crisis. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Since the start
of the RP:P, other projects have aimed to complement our insights about
replicability. Registered Replication Reports focused on single studies, replicated
in much larger sample sizes, to reduce the probability of a Type 2 error. These
studies often quite conclusively showed original studies did not replicate, and
a surprisingly large number yielded findings not statistically different from
0, despite sample sizes much larger than psychologists would be able to collect
in normal research lines. Many Labs studies focused on a smaller set of
studies, replicated many times, sometimes with minor variations to examine the
role of possible moderators proposed to explain failures to replicate, which
were typically absent. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The
collaborative author involved replications are the latest addition to this
expanding literature that consistently shows great difficulties in replicating findings.
I believe they currently make up the steel man for researchers motivated to cast
doubt on the presence of a replication crisis. I believe the fact that these
large projects with direct involvement of the original authors can not find
support for predicted effects are the strongest evidence too date that we have
a problem replicating findings. Of course, these studies are complemented by
Registered Replication Reports and Many Labs studies, and together they make up
the Steel Man to argue against if you are a Replication Crisis Denier. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><b><i>References</i></b> <br /></span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Coles, N. A., March,
D. S., Marmolejo-Ramos, F., Larsen, J., Arinze, N. C., Ndukaihe, I., Willis,
M., Francesco, F., Reggev, N., Mokady, A., Forscher, P. S., Hunter, J.,
Gwenaël, K., Yuvruk, E., Kapucu, A., Nagy, T., Hajdu, N., Tejada, J., Freitag,
R., … Marozzi, M. (2022). <i>A Multi-Lab Test of the Facial Feedback Hypothesis
by The Many Smiles Collaboration</i>. PsyArXiv. <a href="https://doi.org/10.31234/osf.io/cvpuw">https://doi.org/10.31234/osf.io/cvpuw</a></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Klein, R. A., Cook, C.
L., Ebersole, C. R., Vitiello, C., Nosek, B. A., Hilgard, J., Ahn, P. H.,
Brady, A. J., Chartier, C. R., Christopherson, C. D., Clay, S., Collisson, B.,
Crawford, J. T., Cromar, R., Gardiner, G., Gosnell, C. L., Grahe, J., Hall, C.,
Howard, I., … Ratliff, K. A. (2022). Many Labs 4: Failure to Replicate
Mortality Salience Effect With and Without Original Author Involvement. <i>Collabra:
Psychology</i>, <i>8</i>(1), 35271. <a href="https://doi.org/10.1525/collabra.35271">https://doi.org/10.1525/collabra.35271</a></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Morey, R. D., Kaschak,
M. P., Díez-Álamo, A. M., Glenberg, A. M., Zwaan, R. A., Lakens, D., Ibáñez,
A., García, A., Gianelli, C., Jones, J. L., Madden, J., Alifano, F., Bergen,
B., Bloxsom, N. G., Bub, D. N., Cai, Z. G., Chartier, C. R., Chatterjee, A.,
Conwell, E., … Ziv-Crispel, N. (2021). A pre-registered, multi-lab
non-replication of the action-sentence compatibility effect (ACE). <i>Psychonomic
Bulletin & Review</i>. <a href="https://doi.org/10.3758/s13423-021-01927-8">https://doi.org/10.3758/s13423-021-01927-8</a></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span style="mso-ansi-language: #1000;">Vohs, K. D.,
Schmeichel, B. J., Lohmann, S., Gronau, Q. F., Finley, A. J., Ainsworth, S. E.,
Alquist, J. L., Baker, M. D., Brizi, A., Bunyi, A., Butschek, G. J., Campbell,
C., Capaldi, J., Cau, C., Chambers, H., Chatzisarantis, N. L. D., Christensen,
W. J., Clay, S. L., Curtis, J., … Albarracín, D. (2021). A Multisite
Preregistered Paradigmatic Test of the Ego-Depletion Effect. <i>Psychological
Science</i>, <i>32</i>(10), 1566–1581. <a href="https://doi.org/10.1177/0956797621989733">https://doi.org/10.1177/0956797621989733</a></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com0tag:blogger.com,1999:blog-987850932434001559.post-75225129988630649422021-11-20T14:17:00.005+01:002021-12-08T20:51:54.082+01:00Why p-values should be interpreted as p-values and not as measures of evidence<p><!--[if !mso]>
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</p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><i>Update: Florian Hartig has also published a <a href="https://theoreticalecology.wordpress.com/2021/12/08/can-p-values-be-interpreted-as-continuous-measures-of-evidence-for-an-effect/" target="_blank">blog post</a> criticizing the paper by Muff et al (2021). </i><br /></span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In a recent
paper <a href="https://www.sciencedirect.com/science/article/pii/S0169534721002846" target="_blank">Muff, Nilsen, O’Hara, and Nater (2021)</a> propose to implement the recommendation
“</span><span style="mso-ansi-language: #0C00;">to regard P-values as what they
are, namely, continuous measures of statistical</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">evidence</span><span lang="EN-US" style="mso-ansi-language: EN-US;">". This is a surprising
recommendation, given that p-values are not valid measures of evidence </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Royall,
1997)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">. The authors follow Bland </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(2015)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> who suggests that “</span><span style="mso-ansi-language: #0C00;">It</span><span style="mso-ansi-language: EN-US;">
</span><span style="mso-ansi-language: #0C00;">is preferable to think of the
significance test probability</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">as an index of the strength of evidence against
the null</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">hypothesis</span><span lang="EN-US" style="mso-ansi-language: EN-US;">” and proposed verbal labels for p-values in
specific ranges (i.e., p-values above 0.1 are ‘little to no evidence’, p-values
between 0.1 and 0.05 are ‘weak evidence’, etc.). P-values are continuous, but the
idea that they are continuous measures of ‘evidence’ has been criticized </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(e.g., Goodman
& Royall, 1988)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">. If the null-hypothesis is true,
p-values are uniformly distributed. This means it is just as likely to observe
a p-value of 0.001 as it is to observe a p-value of 0.999. This indicates that the
interpretation of p = 0.001 as ‘strong evidence’ cannot be defended just
because the probability to observe this p-value is very small. After all, if
the null hypothesis is true, the probability of observing p = 0.999 is exactly
as small.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The reason that
small p-values can be used to guide us in the direction of true effects is not
because they are rarely observed when the null-hypothesis is true, but because
they are relatively less likely to be observed when the null hypothesis is
true, than when the alternative hypothesis is true. For this reason,
statisticians have argued that the concept of evidence is necessarily ‘relative’.
We can quantify evidence in favor of one hypothesis over another hypothesis,
based on the likelihood of observing data when the null hypothesis is true,
compared to this probability when an alternative hypothesis is true. As Royall
(1997, p. 8) explains: “</span><span style="mso-ansi-language: #0C00;">The law of
likelihood applies to pairs of hypotheses, telling when a</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">given
set of observations is evidence for one versus the other:</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">hypothesis
<i>A </i>is better supported than <i>B </i>if <i>A </i>implies a greater
probability</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">for the observations than <i>B </i>does. This
law represents a concept</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">of evidence that is essentially relative, one
that does not apply to</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">a single hypothesis, taken alone.</span><span lang="EN-US" style="mso-ansi-language: EN-US;">” As Goodman and Royall (1988, p. 1569)
write, “</span><span style="mso-ansi-language: #0C00;">The p-value is not adequate
for inference because the</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">measurement of evidence requires at least three
components:</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">the observations, and two competing explanations
for how</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">they were produced.</span><span lang="EN-US" style="mso-ansi-language: EN-US;">”</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In
practice, the problem of interpreting p-values as evidence in absence of a
clearly defined alternative hypothesis is that they at best serve as proxies
for evidence, but not as a useful measure where a specific p-value can be related to a specific strength of evidence. In some situations, such as when the null hypothesis is true,
p-values are unrelated to evidence. In practice, when researchers examine a mix
of hypotheses where the alternative hypothesis is sometimes true, p-values will
be <i>correlated</i> with measures of evidence. However, this correlation can
be quite weak </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Krueger,
2001)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">, and in general this correlation is
too weak for p-values to function as a valid measure of evidence, where
p-values in a specific range can directly be associated with ‘strong’ or ‘weak’
evidence.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">Why
single p-values cannot be interpreted as the strength of evidence</span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The
evidential value of a single p-value depends on the statistical power of the
test (i.e., on the sample size in combination with the effect size of the
alternative hypothesis). The statistical power expresses the probability of
observing a p-value smaller than the alpha level if the alternative hypothesis
is true. When the null hypothesis is true, statistical power is formally undefined,
but in practice in a two-sided test </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">α</span><span lang="EN-US" style="mso-ansi-language: EN-US;">% of the observed p-values will fall below the alpha level, as p-values
are uniformly distributed under the null-hypothesis. The horizontal grey line
in Figure 1 illustrates the expected p-value distribution for a two-sided
independent <i>t</i>-test if the null-hypothesis is true (or when the observed
effect size Cohen’s d is 0). As every p-value is equally likely, they can not
quantify the strength of evidence against the null hypothesis.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><i><span lang="EN-US" style="mso-ansi-language: EN-US;">Figure
1: P-value distributions for a statistical power of 0% (grey line), 50% (black curve)
and 99% (dotted black curve). </span></i></p><p class="MsoNormal"><i></i></p><div class="separator" style="clear: both; text-align: center;"><i><a href="https://1.bp.blogspot.com/-kuwZbUI3fCQ/YZjz4zdnrKI/AAAAAAAA7FM/zIQddSMHyc4od842TF-ZI4R5rwKMwnNmgCLcBGAsYHQ/s1200/fig_1.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="630" data-original-width="1200" height="336" src="https://1.bp.blogspot.com/-kuwZbUI3fCQ/YZjz4zdnrKI/AAAAAAAA7FM/zIQddSMHyc4od842TF-ZI4R5rwKMwnNmgCLcBGAsYHQ/w640-h336/fig_1.png" width="640" /></a></i></div><i><br /><span lang="EN-US" style="mso-ansi-language: EN-US;"><br /></span></i><span lang="EN-US" style="mso-ansi-language: EN-US;">If the alternative hypothesis is true the strength of evidence that
corresponds to a p-value depends on the statistical power of the test. If power
is 50%, we should expect that 50% of the observed p-values fall below the alpha
level. The remaining p-values fall above the alpha level. The black curve in
Figure 1 illustrates the p-value distribution for a test with a statistical
power of 50% for an alpha level of 5%. A p-value of 0.168 is more likely when
there is a true effect that is examined in a statistical test with 50% power than
when the null hypothesis is true (as illustrated by the black curve being above
the grey line at p = 0.168). In other words, a p-value of 0.168 is evidence <i>for</i>
an alternative hypothesis examined with 50% power, compared to the null
hypothesis.</span><p></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">If an
effect is examined in a test with 99% power (the dotted line in Figure 1) we
would draw a different conclusion. With such high power p-values larger than
the alpha level of 5% are rare (they occur only 1% of the time) and a p-value
of 0.168 is much more likely to be observed when the null-hypothesis is true
than when a hypothesis is examined with 99% power. Thus, a p-value of 0.168 is
evidence <i>against</i> an alternative hypothesis examined with 99% power,
compared to the null hypothesis.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Figure 1
illustrates that with 99% power even a ‘statistically significant’ p-value of
0.04 is evidence <i>for</i> of the null-hypothesis. The reason for this is that
the probability of observing a p-value of 0.04 is more likely when the null
hypothesis is true than when a hypothesis is tested with 99% power (i.e., the
grey horizontal line at p = 0.04 is above the dotted black curve). This fact,
which is often counterintuitive when first encountered, is known as the Lindley
paradox, or the Jeffreys-Lindley paradox </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(for a
discussion, see Spanos, 2013)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Figure 1
illustrates that different p-values can correspond to the same relative
evidence in favor of a specific alternative hypothesis, and that the same
p-value can correspond to different levels of relative evidence. This is
obviously undesirable if we want to use p-values as a measure of the strength
of evidence. Therefore, it is incorrect to verbally label any p-value as
providing ‘weak’, ‘moderate’, or ‘strong’ evidence against the null hypothesis,
as depending on the alternative hypothesis a researcher is interested in, the
level of evidence will differ (and the p-value could even correspond to
evidence in favor of the null hypothesis).</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">All
p-values smaller than 1 correspond to evidence for some non-zero effect</span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">If the
alternative hypothesis is not specified, any p-value smaller than 1 should be
treated as at least <i>some</i> evidence (however small) for <i>some</i>
alternative hypotheses. It is therefore not correct to follow the
recommendations of the authors in their Table 2 to interpret p-values above 0.1
(e.g., a p-value of 0.168) as “no evidence” for a relationship. This also goes against
the arguments by Muff and colleagues that ‘</span><span style="mso-ansi-language: #0C00;">the notion of (accumulated) evidence is the main concept behind</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">meta-analyses</span><span lang="EN-US" style="mso-ansi-language: EN-US;">”. Combining three studies with a
p-value of 0.168 in a meta-analysis is enough to reject the null hypothesis
based on p < 0.05 (see the forest plot in Figure 2). It thus seems
ill-advised to follow their recommendation to describe a single study with p =
0.168 as ‘no evidence’ for a relationship. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><i><span lang="EN-US" style="mso-ansi-language: EN-US;">Figure
2: Forest plot for a meta-analysis of three identical studies yielding p =
0.168.</span></i></p>
<div class="separator" style="clear: both; text-align: center;"><a href="https://1.bp.blogspot.com/-2UotH3MNpCw/YZj0RmUvfMI/AAAAAAAA7Fc/EqQ_MrNgdwEkDPPnWXiNrvvcmAYs03cPQCLcBGAsYHQ/s1494/fig_2.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="806" data-original-width="1494" height="346" src="https://1.bp.blogspot.com/-2UotH3MNpCw/YZj0RmUvfMI/AAAAAAAA7Fc/EqQ_MrNgdwEkDPPnWXiNrvvcmAYs03cPQCLcBGAsYHQ/w640-h346/fig_2.png" width="640" /></a></div><br />
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">However,
replacing the label of ‘no evidence’ with the label ‘at least some evidence for
some hypotheses’ leads to practical problems when communicating the results of
statistical tests. It seems generally undesirable to allow researchers to
interpret any p-value smaller than 1 as ‘at least some evidence’ against the null
hypothesis. This is the price one pays for not specifying an alternative
hypothesis, and try to interpret p-values from a null hypothesis significance
test in an evidential manner. If we do not specify the alternative hypothesis,
it becomes impossible to conclude there is evidence <i>for</i> the null
hypothesis, and we cannot statistically falsify any hypothesis </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Lakens,
Scheel, et al., 2018)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">. Some would argue that if you can not falsify hypotheses, you have a bit of a problem (Popper, 1959). <br /></span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></b></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">Interpreting
p-values as p-values</span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Instead of
interpreting p-values as measures of the strength of evidence, we could
consider a radical alternative: interpret p-values as p-values. This would,
perhaps surprisingly, solve the main problems that Muff and colleagues aim to
address, namely ‘</span><span style="mso-ansi-language: #0C00;">black-or-white
null-hypothesis significance testing with</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">an arbitrary P-value
cutoff</span><span lang="EN-US" style="mso-ansi-language: EN-US;">’. The idea to
interpret p-values as measures of evidence is most strongly tried to a
Fisherian interpretation of p-values. An alternative statistical frequentist
philosophy was developed by Neyman and Pearson </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(1933a)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> who propose to use p-values to
guide decisions about the null and alternative hypothesis by, in the long run,
controlling the Type I and Type II error rate. Researchers specify an alpha
level and design a study with a sufficiently high statistical power, and reject
(or fail to reject) the null hypothesis.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Neyman and
Pearson never proposed to use hypothesis tests as binary yes/no test outcomes. First,
Neyman and Pearson </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(1933b)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> leave open whether the states of
the world are divided in two (‘accept’ and ‘reject’) or three regions, and write
that a “region of doubt may be obtained by a further subdivision of the region
of acceptance”. A useful way to move beyond a yes/no dichotomy in frequentist statistics
is to test range predictions instead of limiting oneself to a null hypothesis
significance test </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Lakens,
2021)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">. This implements the idea of Neyman
and Pearson to introduce a region of doubt, and distinguishes inconclusive results
(where neither the null hypothesis nor the alternative hypothesis can be
rejected, and more data needs to be collected to draw a conclusion) from
conclusive results (where either the null hypothesis or the alternative
hypothesis can be rejected. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In a
Neyman-Pearson approach to hypothesis testing the act of rejecting a hypothesis
comes with a maximum long run probability of doing so in error. </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman";">As Hacking (1965) writes: “Rejection is not refutation. Plenty of
rejections must be only tentative.” So when we reject the null model, we do so
tentatively, aware of the fact we might have done so in error, and without
necessarily believing the null model is false. For Neyman </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(1957, p.
13)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> inferential behavior is an: “act of
will to behave in the future (perhaps until new experiments are performed) in a
particular manner, conforming with the outcome of the experiment”. </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman";">All knowledge in science is provisional.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman";">Furthermore, it is important to remember that hypothesis
tests reject a statistical hypothesis, but not a theoretical hypothesis. As
Neyman </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(1960, p.
290)</span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman";"> writes: “</span><span style="mso-ansi-language: #0C00; mso-bidi-font-family: "Times New Roman";">the frequency</span><span style="mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman";"> </span><span style="mso-ansi-language: #0C00; mso-bidi-font-family: "Times New Roman";">of correct conclusions regarding the
statistical hypothesis tested may be</span><span style="mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman";"> </span><span style="mso-ansi-language: #0C00; mso-bidi-font-family: "Times New Roman";">in perfect agreement</span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman";"> with</span><span style="mso-ansi-language: #0C00; mso-bidi-font-family: "Times New Roman";"> the predictions of the power function, but not</span><span style="mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman";"> </span><span style="mso-ansi-language: #0C00; mso-bidi-font-family: "Times New Roman";">the
frequency of correct conclusions regarding the primary hypothesis</span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman";">”. In other words, whether or not we can reject a statistical hypothesis
in a specific experiment does not necessarily inform us about the truth of the theory.
Decisions about the truthfulness of a theory requires a careful evaluation of
the auxiliary hypotheses upon which the experimental procedure is built </span><span lang="EN-US" style="line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: Arial; mso-bidi-font-size: 12.0pt;">(Uygun Tunç & Tunç, 2021)</span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman";">.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Neyman </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(1976)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> provides some reporting examples that
reflect his philosophy on statistical inferences: “</span><span style="mso-ansi-language: #0C00;">after considering the</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">probability
of error (that is, after considering how frequently</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">we
would be in error if in conditions of our data we rejected the</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">hypotheses
tested), we decided to act on the assumption that</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">"high" scores on
"potential</span><span lang="EN-US" style="mso-ansi-language: EN-US;">”</span><span style="mso-ansi-language: #0C00;"> and on "education" are indicative of</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">better
chances of success in the drive to home ownership</span><span lang="EN-US" style="mso-ansi-language: EN-US;">”. An example of a shorter statement that
Neyman provides reads: “</span><span style="mso-ansi-language: #0C00;">As a
result of the tests we applied,</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">we decided to act on the assumption (or
concluded) that the two</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">groups are not random samples from the same
population.</span><span lang="EN-US" style="mso-ansi-language: EN-US;">” </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">A complete
verbal description of the result of a Neyman-Pearson hypothesis test acknowledges
two sources of uncertainty. First, the assumptions of the statistical test must
be met (i.e., data is normally distributed), or any deviations should be small
enough to not have any substantial effect on the frequentist error rates. Second,
conclusions are made “</span><span style="mso-ansi-language: #0C00;">Without
hoping</span><span style="mso-ansi-language: EN-US;"> </span><span style="mso-ansi-language: #0C00;">to know. whether each separate hypothesis is
true or false</span><span lang="EN-US" style="mso-ansi-language: EN-US;">” </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Neyman
& Pearson, 1933a)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">. Any single conclusion can be
wrong, and assuming the test assumption are met, we make claims under a known maximum
error rate (which is never zero). Future replication studies are needed to provide
further insights about whether the current conclusion was erroneous or not.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">After observing
a p-value smaller than the alpha level, one can therefore conclude: “Until new
data emerges that proves us wrong, we decide to act as if there is an effect,
while acknowledging that the methodological procedure we base this decision on
has, a maximum error rate of alpha% (assuming the statistical assumptions are
met), which we find acceptably low.” One can follow such a statement about the
observed data with a theoretical inference, such as “assuming our auxiliary
hypotheses hold, the result of this statistical test corroborates our
theoretical hypothesis”. If a conclusive test result in an equivalence test is
observed that allows a researcher to reject the presence of any effect large
enough to be meaningful, the conclusion would be that the test result does not
corroborate the theoretical hypothesis. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The problem
that the common application of null hypothesis significance testing in science
is based on an arbitrary threshold of 0.05 is true </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Lakens,
Adolfi, et al., 2018)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">. There are surprisingly few
attempts to provide researchers with practical approaches to determine an alpha
level on more substantive grounds (but see </span><span lang="NL" style="mso-bidi-font-family: Arial;">Field et al., 2004; Kim & Choi, 2021; Maier & Lakens, 2021;
Miller & Ulrich, 2019; Mudge et al., 2012)</span><span lang="NL">. </span><span lang="EN-US" style="mso-ansi-language: EN-US;">It seems
difficult to resolve in practice, both because at least some scientist adopt a
philosophy of science where the goal of hypothesis tests is to establish a
corpus of scientific claims </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Frick,
1996)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">, and any continuous measure will be
broken up in a threshold below which a researcher are not expected to make a
claim about a finding (e.g., a BF < 3, see </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Kass
& Raftery, 1995</span><span lang="EN-US" style="mso-ansi-language: EN-US;">, or a likelihood ratio lower than k
= 8, see </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Royall, 2000)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">. Although it is true that an alpha
level of 0.05 is arbitrary, there are some pragmatic arguments in its favor
(e.g., it is established, and it might be low enough to yield claims that are
taken seriously, but not high enough to prevent other researchers from attempting
to refute the claim, see </span><span lang="EN-US" style="line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: Arial; mso-bidi-font-size: 12.0pt;">Uygun Tunç et al., 2021)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">If there
really no agreement on best practices in sight?</span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">One major
impetus for the flawed proposal to interpret p-values as evidence by Muff and
colleagues is that “</span><span style="mso-ansi-language: #0C00;">no agreement
on a way forward is in sight</span><span lang="EN-US" style="mso-ansi-language: EN-US;">”. The statement that there is little agreement among statisticians is
an oversimplification. I will go out on a limb and state some things I assume
most statisticians agree on. First, there are multiple statistical tools one
can use, and each tool has their own strengths and weaknesses. Second, there
are different statistical philosophies, each with their own coherent logic, and
researchers are free to analyze data from the perspective of one or multiple of
these philosophies. Third, one should not misuse statistical tools, or apply
them to attempt to answer questions the tool was not designed to answer.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">It is true
that there is variation in the preferences individuals have about which
statistical tools should be used, and the philosophies of statistical
researchers should adopt. This should not be surprising. Individual researchers
differ in which research questions they find interesting within a specific
content domain, and similarly, they differ in which statistical questions they
find interesting when analyzing data. Individual researchers differ in which
approaches to science they adopt (e.g., a qualitative or a quantitative
approach), and similarly, they differ in which approach to statistical
inferences they adopt (e.g., a frequentist or Bayesian approach). Luckily, there
is no reason to limit oneself to a single tool or philosophy, and if anything,
the recommendation is to use multiple approaches to statistical inferences. It
is not always interesting to ask what the p-value is when analyzing data, and
it is often interesting to ask what the effect size is. Researchers can believe
it is important for reliable knowledge generation to control error rates when
making scientific claims, while at the same time believing that it is important
to quantify relative evidence using likelihoods or Bayes factors (for example
by presented a Bayes factor alongside every p-value for a statistical test, </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Lakens et
al., 2020)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Whatever approach
to statistical inferences researchers choose to use, the approach should answer
a meaningful statistical question </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Hand,
1994)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">, the approach to statistical
inferences should be logically coherent, and the approach should be applied
correctly. Despite the common statement in the literature that p-values can be
interpreted as measures of evidence, the criticism against the coherence of
this approach should make us pause. Given that coherent alternatives exist,
such as likelihoods </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Royall,
1997)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> or Bayes factors </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Kass
& Raftery, 1995)</span><span lang="EN-US" style="mso-ansi-language: EN-US;">, researchers should not follow the
recommendation by Muff and colleagues to report p = 0.08 as ‘weak evidence’, p
= 0.03 as ‘moderate evidence’, and p = 0.168 as ‘no evidence’.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">References</span></b></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Bland, M.
(2015). <i>An introduction to medical statistics</i> (Fourth edition). Oxford
University Press.</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Field, S. A., Tyre,
A. J., Jonzén, N., Rhodes, J. R., & Possingham, H. P. (2004). Minimizing
the cost of environmental management decisions by optimizing statistical
thresholds. <i>Ecology Letters</i>, <i>7</i>(8), 669–675.
https://doi.org/10.1111/j.1461-0248.2004.00625.x</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Frick, R. W. (1996).
The appropriate use of null hypothesis testing. <i>Psychological Methods</i>, <i>1</i>(4),
379–390. https://doi.org/10.1037/1082-989X.1.4.379</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Goodman, S. N.,
& Royall, R. (1988). Evidence and scientific research. <i>American Journal
of Public Health</i>, <i>78</i>(12), 1568–1574.</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Hand, D. J. (1994).
Deconstructing Statistical Questions. <i>Journal of the Royal Statistical
Society. Series A (Statistics in Society)</i>, <i>157</i>(3), 317–356.
https://doi.org/10.2307/2983526</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Kass, R. E., &
Raftery, A. E. (1995). Bayes factors. <i>Journal of the American Statistical
Association</i>, <i>90</i>(430), 773–795.
https://doi.org/10.1080/01621459.1995.10476572</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Kim, J. H., &
Choi, I. (2021). Choosing the Level of Significance: A Decision-theoretic
Approach. <i>Abacus</i>, <i>57</i>(1), 27–71.
https://doi.org/10.1111/abac.12172</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Krueger, J. (2001).
Null hypothesis significance testing: On the survival of a flawed method. <i>American
Psychologist</i>, <i>56</i>(1), 16–26.
https://doi.org/10.1037//0003-066X.56.1.16</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Lakens, D. (2021).
The practical alternative to the p value is the correctly used p value. <i>Perspectives
on Psychological Science</i>, <i>16</i>(3), 639–648.
https://doi.org/10.1177/1745691620958012</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Lakens, D., Adolfi,
F. G., Albers, C. J., Anvari, F., Apps, M. A. J., Argamon, S. E., Baguley, T.,
Becker, R. B., Benning, S. D., Bradford, D. E., Buchanan, E. M., Caldwell, A.
R., Calster, B., Carlsson, R., Chen, S.-C., Chung, B., Colling, L. J., Collins,
G. S., Crook, Z., … Zwaan, R. A. (2018). Justify your alpha. <i>Nature Human
Behaviour</i>, <i>2</i>, 168–171. https://doi.org/10.1038/s41562-018-0311-x</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="NL" style="mso-bidi-font-family: Arial;">Lakens, D., McLatchie, N., Isager, P. M.,
Scheel, A. M., & Dienes, Z. (2020). </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Improving Inferences
About Null Effects With Bayes Factors and Equivalence Tests. <i>The Journals of
Gerontology: Series B</i>, <i>75</i>(1), 45–57.
https://doi.org/10.1093/geronb/gby065</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="NL" style="mso-bidi-font-family: Arial;">Lakens, D., Scheel, A. M., & Isager, P.
M. (2018). </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Equivalence testing for psychological research: A
tutorial. <i>Advances in Methods and Practices in Psychological Science</i>, <i>1</i>(2),
259–269. https://doi.org/10.1177/2515245918770963</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Maier, M., &
Lakens, D. (2021). <i>Justify Your Alpha: A Primer on Two Practical Approaches</i>.
</span><span lang="NL" style="mso-bidi-font-family: Arial;">PsyArXiv.
https://doi.org/10.31234/osf.io/ts4r6</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="NL" style="mso-bidi-font-family: Arial;">Miller, J., & Ulrich, R. (2019). </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">The quest
for an optimal alpha. <i>PLOS ONE</i>, <i>14</i>(1), e0208631.
https://doi.org/10.1371/journal.pone.0208631</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Mudge, J. F., Baker,
L. F., Edge, C. B., & Houlahan, J. E. (2012). Setting an Optimal </span><span lang="NL" style="mso-bidi-font-family: Arial;">α</span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;"> That Minimizes
Errors in Null Hypothesis Significance Tests. <i>PLOS ONE</i>, <i>7</i>(2),
e32734. https://doi.org/10.1371/journal.pone.0032734</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Neyman, J. (1957).
“Inductive Behavior” as a Basic Concept of Philosophy of Science. <i>Revue de
l’Institut International de Statistique / Review of the International
Statistical Institute</i>, <i>25</i>(1/3), 7–22.
https://doi.org/10.2307/1401671</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Neyman, J. (1960). <i>First
course in probability and statistics</i>. Holt, Rinehart and Winston.</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Neyman, J. (1976).
Tests of statistical hypotheses and their use in studies of natural phenomena. <i>Communications
in Statistics - Theory and Methods</i>, <i>5</i>(8), 737–751.
https://doi.org/10.1080/03610927608827392</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Neyman, J., &
Pearson, E. S. (1933a). On the problem of the most efficient tests of
statistical hypotheses. <i>Philosophical Transactions of the Royal Society of
London A: Mathematical, Physical and Engineering Sciences</i>, <i>231</i>(694–706),
289–337. https://doi.org/10.1098/rsta.1933.0009</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Neyman, J., &
Pearson, E. S. (1933b). The testing of statistical hypotheses in relation to
probabilities a priori. <i>Mathematical Proceedings of the Cambridge
Philosophical Society</i>, <i>29</i>(04), 492–510.
https://doi.org/10.1017/S030500410001152X</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Royall, R. (1997). <i>Statistical
Evidence: A Likelihood Paradigm</i>. Chapman and Hall/CRC.</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Royall, R. (2000).
On the probability of observing misleading statistical evidence. <i>Journal of
the American Statistical Association</i>, <i>95</i>(451), 760–768.</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Spanos, A. (2013).
Who should be afraid of the Jeffreys-Lindley paradox? <i>Philosophy of Science</i>,
<i>80</i>(1), 73–93.</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">Uygun Tunç, D.,
& Tunç, M. N. (2021). A Falsificationist Treatment of Auxiliary Hypotheses
in Social and Behavioral Sciences: Systematic Replications Framework. </span><span lang="NL" style="mso-bidi-font-family: Arial;">In <i>Meta-Psychology</i>.
https://doi.org/10.31234/osf.io/pdm7y</span></p>
<p class="MsoBibliography" style="line-height: normal;"><span lang="NL" style="mso-bidi-font-family: Arial;">Uygun Tunç, D., Tunç, M. N., & Lakens,
D. (2021). </span><i><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">The Epistemic and Pragmatic Function of Dichotomous
Claims Based on Statistical Hypothesis Tests</span></i><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">. </span><span lang="NL" style="mso-bidi-font-family: Arial;">PsyArXiv.
https://doi.org/10.31234/osf.io/af9by</span></p>
Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com10tag:blogger.com,1999:blog-987850932434001559.post-30918317640300772732021-10-31T10:12:00.005+01:002021-10-31T19:43:07.811+01:00Not All Flexibility P-Hacking Is, Young Padawan
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">During a recent
workshop on Sample Size Justification an early career researcher asked me: “You
recommend sequential analysis in your <a href="https://psyarxiv.com/9d3yf/">paper</a>
for when effect sizes are uncertain, where researchers collect data, analyze the
data, stop when a test is significant, or continue data collection when a test
is not significant, and, I don’t want to be rude, but isn’t this p-hacking?”</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In
linguistics there is a term for when children apply a rule they have learned to
instances where it does not apply: Overregularization. They learn ‘one cow, two
cows’, and use the +s rule for plural where it is not appropriate, such as ‘one
mouse, two mouses’ (instead of ‘two mice’). The early career researcher who
asked me if sequential analysis was a form of p-hacking was also overregularizing.
We teach young researchers that flexibly analyzing data inflates error rates,
is called p-hacking, and is a very bad thing that was one of the causes of the
replication crisis. So, they apply the rule ‘flexibility in the data analysis
is a bad thing’ to cases where it does not apply, such as in the case of
sequential analyses. Yes, sequential analyses give a lot of flexibility to stop
data collection, but it does so while carefully controlling error rates, with
the added bonus that it can increase the efficiency of data collection. This
makes it a good thing, not p-hacking. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span><img id="done-img" src="https://i.imgflip.com/5sgmw7.jpg" /></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Children
increasingly use correct language the longer they are immersed in it. Many
researchers are not yet immersed in an academic environment where they see flexibility
in the data analysis applied correctly. Many are scared to do things wrong,
which risks becoming overly conservative, as the pendulum from ‘we are all p-hacking
without realizing the consequences’ swings back to far to ‘all flexibility is
p-hacking’. Therefore, I patiently explain during workshops that flexibility is
not bad per se, but that making claims without controlling your error rate is
problematic.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In a recent
podcast episode of ‘<a href="https://quantitudepod.org/">Quantitude</a>’ one of
the hosts shared a similar experience 5 minutes into the episode. A young
student remarked that flexibility during the data analysis was ‘unethical’. The
remainder of the podcast episode on ‘researcher degrees of freedom’ discussed
how flexibility is part of data analysis. They clearly state that p-hacking is
problematic, and opportunistic motivations to perform analyses that give you
what you want to find should be constrained. But they then criticized
preregistration in ways many people on </span><span lang="NL"><a href="https://twitter.com/quantitudepod/status/1450433765730357248?s=20"><span lang="EN-US" style="mso-ansi-language: EN-US;">Twitter</span></a></span><span lang="EN-US" style="mso-ansi-language: EN-US;"> disagreed with. They talk about
‘high priests’ who want to ‘stop bad people from doing bad things’ which they
find uncomfortable, and say ‘you can not preregister every contingency’. They
remark they would be surprised if data could be analyzed without requiring any on
the fly judgment. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Although
the examples they gave were not very good<sup>1</sup> it is of course true that
researchers sometimes need to deviate from an analysis plan. Deviating from an
analysis plan is not p-hacking. But when people talk about preregistration, we often
see overregularization: “Preregistration requires specifying your analysis plan
to prevent inflation of the Type 1 error rate, so deviating from a
preregistration is not allowed.” </span><span lang="NL"><a href="https://philstatwars.files.wordpress.com/2020/08/lakens_jpr623221-230_2019_red.pdf"><span lang="EN-US" style="mso-ansi-language: EN-US;">The whole point of preregistration</span></a></span><span lang="EN-US" style="mso-ansi-language: EN-US;"> is to transparently allow other
researchers to evaluate the severity of a test, both when you stick to the
preregistered statistical analysis plan, as when you deviate from it. Some
researchers have sufficient experience with the research they do that they can
preregister an analysis that does not require any deviations<sup>2</sup>, and
then readers can see that the Type 1 error rate for the study is at the level
specified before data collection. Other researchers will need to deviate from their
analysis plan because they encounter unexpected data. Some deviations reduce
the severity of the test by inflating the Type 1 error rate. But other
deviations actually get you closer to the truth. We can not know which is
which. A reader needs to form their own judgment about this.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">A final example
of overregularization comes from a person who discussed a new study that they
were preregistering with a junior colleague. They mentioned the possibility of including
a covariate in an analysis but thought that was too exploratory to be included
in the preregistration. The junior colleague remarked: “But now that we have
thought about the analysis, we need to preregister it”. Again, we see an
example of overregularization. If you want to control the Type 1 error rate in
a test, preregister it, and follow the preregistered statistical analysis plan.
But researchers can, and should, explore data to <i>generate</i> hypotheses
about things that are going on in their data. You can preregister these, but
you do not have to. Not exploring data could even be seen as research waste, as
you are missing out on the opportunity to generate hypotheses that are informed
by data. A case can be made that researchers should regularly include variables
to explore (e.g., measures that are of general interest to peers in their
field), as long as these do not interfere with the primary hypothesis test (and
as long as these explorations are presented as such). </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In the book
“Reporting quantitative research in psychology: How to meet APA Style Journal
Article Reporting Standards” by Cooper and colleagues from 2020 a very useful distinction
is made between primary hypotheses, secondary hypotheses, and exploratory
hypotheses. The first consist of the main tests you are designing the study
for. The secondary hypotheses are also of interest when you design the study –
but you might not have sufficient power to detect them. You did not design the
study to test these hypotheses, and because the power for these tests might be
low, you did not control the Type 2 error rate for secondary hypotheses. You <i>can</i>
preregister secondary hypotheses to control the Type 1 error rate, as you know
you will perform them, and if there are multiple secondary hypotheses, as
Cooper et al (2020) remark, readers will expect “adjusted levels of statistical
significance, or conservative post hoc means tests, when you conducted your
secondary analysis”. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">If you
think of the possibility to analyze a covariate, but decide this is an
exploratory analysis, you can decide to neither control the Type 1 error rate
nor the Type 2 error rate. These are <i>analyses</i>, but not <i>tests of a hypothesis</i>,
as any findings from these analyses have an unknown Type 1 error rate. Of
course, that does not mean these analyses can not be correct in what they
reveal – we just have no way to know the long run probability that exploratory
conclusions are wrong. Future tests of the hypotheses generated in exploratory
analyses are needed. But as long as you follow Journal Article Reporting
Standards and distinguish exploratory analyses, readers know what the are
getting. Exploring is not p-hacking. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">People in
psychology are re-learning the basic rules of hypothesis testing in the wake of
the replication crisis. But because they are not yet immersed in good research
practices, the lack of experience means they are overregularizing simplistic
rules to situations where they do not apply. Not all flexibility is p-hacking, preregistered
studies do not prevent you from deviating from your analysis plan, and you do
not need to preregister every possible test that you think of. A good cure for
overregularization is reasoning from basic principles. Do not follow simple
rules (or what you see in published articles) but make decisions based on an understanding
of how to achieve your inferential goal. If the goal is to make claims with
controlled error rates, prevent Type 1 error inflation, for example by
correcting the alpha level where needed. If your goal is to explore data, feel
free to do so, but know these explorations should be reported as such. When you
design a study, follow the Journal Article Reporting Standards and distinguish
tests with different inferential goals. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<p class="MsoNormal"><sup><span lang="EN-US" style="mso-ansi-language: EN-US;">1</span></sup><span lang="EN-US" style="mso-ansi-language: EN-US;"> E.g., they discuss having to choose
between Student’s t-test and Welch’s t-test, depending on wheter Levene’s test
indicates the assumption of homogeneity is violated, which is not best practice
– just follow R, and use </span><span lang="NL"><a href="https://daniellakens.blogspot.com/2015/01/always-use-welchs-t-test-instead-of.html"><span lang="EN-US" style="mso-ansi-language: EN-US;">Welch’s t-test by default</span></a></span><span lang="EN-US" style="mso-ansi-language: EN-US;">.</span></p>
<p class="MsoNormal"><sup><span lang="EN-US" style="mso-ansi-language: EN-US;">2</span></sup><span lang="EN-US" style="mso-ansi-language: EN-US;"> But this is rare – only 2 out of 27
preregistered studies in Psychological Science made no deviations. <a href="https://royalsocietypublishing.org/doi/full/10.1098/rsos.211037">https://royalsocietypublishing.org/doi/full/10.1098/rsos.211037</a>
We can probably do a bit better if we only preregistered predictions at a time
where we really understand our manipulations and measures. </span></p>
Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com3tag:blogger.com,1999:blog-987850932434001559.post-29773184016494013362021-09-20T20:19:00.014+02:002021-09-22T10:07:51.414+02:00Jerzy Neyman: A Positive Role Model in the History of Frequentist Statistics<p><!--[if gte mso 9]><xml>
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="toa heading"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Bullet"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List 5"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Bullet 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Bullet 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Bullet 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Bullet 5"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number 5"/>
<w:LsdException Locked="false" Priority="10" QFormat="true" Name="Title"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Closing"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Signature"/>
<w:LsdException Locked="false" Priority="1" SemiHidden="true"
UnhideWhenUsed="true" Name="Default Paragraph Font"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text Indent"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Continue"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Continue 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Continue 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Continue 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Continue 5"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Message Header"/>
<w:LsdException Locked="false" Priority="11" QFormat="true" Name="Subtitle"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Salutation"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Date"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text First Indent"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text First Indent 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Note Heading"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text Indent 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text Indent 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Block Text"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Hyperlink"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="FollowedHyperlink"/>
<w:LsdException Locked="false" Priority="22" QFormat="true" Name="Strong"/>
<w:LsdException Locked="false" Priority="20" QFormat="true" Name="Emphasis"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Document Map"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Plain Text"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="E-mail Signature"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Top of Form"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Bottom of Form"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Normal (Web)"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Acronym"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Address"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Cite"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Code"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Definition"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Keyboard"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Preformatted"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Sample"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Typewriter"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Variable"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Normal Table"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="annotation subject"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="No List"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Outline List 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Outline List 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Outline List 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Simple 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Simple 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Simple 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Classic 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Classic 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Classic 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Classic 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Colorful 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Colorful 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Colorful 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Columns 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Columns 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Columns 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Columns 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Columns 5"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 5"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 6"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 7"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 8"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 5"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 6"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 7"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 8"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table 3D effects 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table 3D effects 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table 3D effects 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Contemporary"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Elegant"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Professional"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Subtle 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Subtle 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Web 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Web 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Web 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Balloon Text"/>
<w:LsdException Locked="false" Priority="39" Name="Table Grid"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Theme"/>
<w:LsdException Locked="false" SemiHidden="true" Name="Placeholder Text"/>
<w:LsdException Locked="false" Priority="1" QFormat="true" Name="No Spacing"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading"/>
<w:LsdException Locked="false" Priority="61" Name="Light List"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 1"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 1"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 1"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 1"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 1"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 1"/>
<w:LsdException Locked="false" SemiHidden="true" Name="Revision"/>
<w:LsdException Locked="false" Priority="34" QFormat="true"
Name="List Paragraph"/>
<w:LsdException Locked="false" Priority="29" QFormat="true" Name="Quote"/>
<w:LsdException Locked="false" Priority="30" QFormat="true"
Name="Intense Quote"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 1"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 1"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 1"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 1"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 1"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 1"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 1"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 1"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 2"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 2"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 2"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 2"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 2"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 2"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 2"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 2"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 2"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 2"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 2"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 2"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 2"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 2"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 3"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 3"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 3"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 3"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 3"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 3"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 3"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 3"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 3"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 3"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 3"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 3"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 3"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 3"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 4"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 4"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 4"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 4"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 4"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 4"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 4"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 4"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 4"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 4"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 4"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 4"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 4"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 4"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 5"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 5"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 5"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 5"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 5"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 5"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 5"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 5"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 5"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 5"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 5"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 5"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 5"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 5"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 6"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 6"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 6"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 6"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 6"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 6"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 6"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 6"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 6"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 6"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 6"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 6"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 6"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 6"/>
<w:LsdException Locked="false" Priority="19" QFormat="true"
Name="Subtle Emphasis"/>
<w:LsdException Locked="false" Priority="21" QFormat="true"
Name="Intense Emphasis"/>
<w:LsdException Locked="false" Priority="31" QFormat="true"
Name="Subtle Reference"/>
<w:LsdException Locked="false" Priority="32" QFormat="true"
Name="Intense Reference"/>
<w:LsdException Locked="false" Priority="33" QFormat="true" Name="Book Title"/>
<w:LsdException Locked="false" Priority="37" SemiHidden="true"
UnhideWhenUsed="true" Name="Bibliography"/>
<w:LsdException Locked="false" Priority="39" SemiHidden="true"
UnhideWhenUsed="true" QFormat="true" Name="TOC Heading"/>
<w:LsdException Locked="false" Priority="41" Name="Plain Table 1"/>
<w:LsdException Locked="false" Priority="42" Name="Plain Table 2"/>
<w:LsdException Locked="false" Priority="43" Name="Plain Table 3"/>
<w:LsdException Locked="false" Priority="44" Name="Plain Table 4"/>
<w:LsdException Locked="false" Priority="45" Name="Plain Table 5"/>
<w:LsdException Locked="false" Priority="40" Name="Grid Table Light"/>
<w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark"/>
<w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful"/>
<w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 1"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 1"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 1"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 1"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 1"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 1"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 1"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 2"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 2"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 2"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 2"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 2"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 2"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 2"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 3"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 3"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 3"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 3"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 3"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 3"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 3"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 4"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 4"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 4"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 4"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 4"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 4"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 4"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 5"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 5"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 5"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 5"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 5"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 5"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 5"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 6"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 6"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 6"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 6"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 6"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 6"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 6"/>
<w:LsdException Locked="false" Priority="46" Name="List Table 1 Light"/>
<w:LsdException Locked="false" Priority="47" Name="List Table 2"/>
<w:LsdException Locked="false" Priority="48" Name="List Table 3"/>
<w:LsdException Locked="false" Priority="49" Name="List Table 4"/>
<w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark"/>
<w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful"/>
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</p><p class="MsoNormal"><i><span lang="EN-US" style="mso-ansi-language: EN-US;">Many of
the facts in this blog post come from the biography </span></i><span lang="NL"><a href="https://www.springer.com/gp/book/9780387983578"><i><span lang="EN-US" style="mso-ansi-language: EN-US;">‘Neyman’ by Constance Reid</span></i></a></span><i><span lang="EN-US" style="mso-ansi-language: EN-US;">. I highly recommend reading this
book if you find this blog interesting. </span></i></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In recent
years researchers have become increasingly interested in the relationship
between eugenics and statistics, especially focusing on the lives of Francis
Galton, Karl Pearson, and Ronald Fisher. Some have gone as far as to argue for
a causal relationship between eugenics and frequentist statistics. For example,
in a recent book ‘<a href="https://aubreyclayton.com/bernoulli">Bernouilli’s
Fallacy</a>’, Aubrey Clayton speculates that Fisher’s decision to reject prior
probabilities and embrace a frequentist approach was “also at least partly
political”. Rejecting prior probabilities, Clayton argues, makes science seem
more ‘objective’, which would have helped Ronald Fisher and his predecessors to
establish eugenics as a scientific discipline, despite the often-racist
conclusions eugenicists reached in their work. </span></p>When I was asked to review an early version of Clayton’s book for Columbia University Press, I thought that the main narrative was rather unconvincing, and thought the presented history of frequentist statistics was too one-sided and biased. Authors who link statistics to problematic political views often do not mention equally important figures in the history of frequentist statistics who were in all ways the opposite of Ronald Fisher. In this blog post, I want to briefly discuss the work and life of Jerzy Neyman, for two reasons.<p class="MsoNormal" style="text-align: center;"><img src="https://statistics.berkeley.edu/sites/default/files/styles/crop_person/public/faculty/splawa_original.jpg?h=86d7f4b4&itok=1tHrk1mG" /><br /><span style="font-size: x-small;">Jerzy Neyman (image from https://statistics.berkeley.edu/people/jerzy-neyman)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"><span style="font-size: x-small;"> </span><br /></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">First, the
focus on Fisher’s role in the history of frequentist statistics is surprising,
given that the dominant approach to frequentist statistics used in many
scientific disciplines is the Neyman-Pearson approach. If you have ever
rejected a null hypothesis because a <i>p</i>-value was smaller than an alpha level,
or if you have performed a power analysis, you have used the Neyman-Pearson
approach to frequentist statistics, and not the Fisherian approach. Neyman and
Fisher disagreed vehemently about their statistical philosophies (in 1961
Neyman published an article titled ‘</span><span lang="NL"><a href="http://www.orsj.or.jp/~archive/pdf/e_mag/Vol.03_04_145.pdf"><span lang="EN-US" style="mso-ansi-language: EN-US;">Silver Jubilee of My Dispute with
Fisher</span></a></span><span lang="EN-US" style="mso-ansi-language: EN-US;">’), but
it was Neyman’s philosophy that won out and became the default approach to
hypothesis testing in most fields<a href="#_edn1" name="_ednref1" style="mso-endnote-id: edn1;" title=""><span class="MsoEndnoteReference"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="EN-US" style="font-size: 11pt; line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[i]</span></span></span></span></a>.
Anyone discussing the history of frequentist hypothesis testing should
therefore seriously engage with the work of Jerzy Neyman and Egon Pearson. Their
work was not in line with the views of Karl Pearson, Egon's father, nor the views of Fisher.
Indeed, it was a great source of satisfaction to Neyman that their seminal
1933 paper was presented to the Royal Society by Karl Pearson, who was hostile
and skeptical of the work, and (as Neyman thought) reviewed by Fisher<a href="#_edn2" name="_ednref2" style="mso-endnote-id: edn2;" title=""><span class="MsoEndnoteReference"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="EN-US" style="font-size: 11pt; line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[ii]</span></span></span></span></a>,
who strongly disagreed with their philosophy of statistics.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Second,
Jerzy Neyman was also the opposite to Fisher in his political viewpoints.
Instead of promoting eugenics, Neyman worked to improve the position of those
less privileged throughout his life, teaching disadvantaged people in Poland, and
creating educational opportunities for Americans at UC Berkeley. He hired David
Blackwell, who was the first Black tenured faculty member at UC Berkeley. This
is important, because it falsifies the idea put forward by Clayton<a href="#_edn3" name="_ednref3" style="mso-endnote-id: edn3;" title=""><span class="MsoEndnoteReference"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="EN-US" style="font-size: 11pt; line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[iii]</span></span></span></span></a>
that frequentist statistics became the dominant approach in science because the
most important scientists who worked on it wanted to pretend their dubious
viewpoints were based on ‘objective’ scientific methods. <span style="mso-spacerun: yes;"> </span></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">I think it
is useful to broaden the discussion of the history of statistics, beyond the
work by Fisher and Karl Pearson, and credit the work of others<a href="#_edn4" name="_ednref4" style="mso-endnote-id: edn4;" title=""><span class="MsoEndnoteReference"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="EN-US" style="font-size: 11pt; line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[iv]</span></span></span></span></a>
who contributed in at least as important ways to the statistics we use today. I
am continually surprised about how few people working outside of statistics even
know the name of Jerzy Neyman, even though they regularly use his insights when
testing hypotheses. In this blog, I will try to describe his work and life to
add some balance to the history of statistics that most people seem to learn
about. And more importantly, I hope Jerzy Neyman can be a positive role-model
for young frequentist statisticians, who might so far have only been educated
about the life of Ronald Fisher. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><br /></span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">Neyman’s
personal life</span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><br /></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Neyman was
born in 1894 in Russia, but raised in Poland. After attending the gymnasium, he
studied at the University of Kharkov. Initially trying to become an
experimental physicist, he was too clumsy with his hands, and switched to
conceptual mathematics, in which he concluded his undergraduate in 1917 in
politically tumultuous times. In 1919 he met his wife, and they marry in 1920. Ten
days later, because of the war between Russia and Poland, Neyman is imprisoned
for a short time, and in 1921 flees to a small village to avoid being arrested
again, where he obtains food by teaching the children of farmers. He worked for
the Agricultural Institute, and then worked at the University in Warsaw. He
obtained his doctor’s degree in 1924 at age 30. In September 1925 he was sent
to London for a year to learn about the latest developments in statistics from
Karl Pearson himself. It is here that he met Egon Pearson, Karl’s son, and a
friendship and scientific collaboration starts.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Neyman always
spends a lot of time teaching, often at the expense of doing scientific work.
He was involved in equal opportunity education in 1918 in Poland, teaching in
dimly lit classrooms where the rag he used to wipe the blackboard would
sometimes freeze. He always had a weak spot for intellectuals from
‘disadvantaged’ backgrounds. He and his wife were themselves very poor until he
moved to UC Berkeley in 1938. In 1929, back in Poland, his wife becomes ill due
to their bad living conditions, and the doctor who comes to examine her is so
struck by their miserable living conditions he offers the couple stay in his
house for the same rent they were paying while he visits France for 6 months.
In his letters to Egon Pearson from this time, he often complained that the
struggle for existence takes all his time and energy, and that he can not do
any scientific work. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Even much
later in his life, in 1978, he kept in mind that many people have very little money,
and he calls ahead to restaurants to make sure a dinner before a seminar would
not cost too much for the students. It is perhaps no surprise that most of his
students (and he had many) talk about Neyman with a lot of appreciation. He
wasn’t perfect (for example, Erich Lehmann - one of Neyman's students - remarks how he was no longer allowed to
teach a class after Lehmann's notes, building on but extending the work by Neyman,
became extremely popular – suggesting Neyman was no stranger to envy). But his
students were extremely positive about the atmosphere he created in his lab.
For example, job applicants were told around 1947 that “there is no
discrimination on the basis of age, sex, or race ... authors of joint papers
are always listed alphabetically."</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Neyman
himself often suffered discrimination, sometimes because of his difficulty mastering
the English language, sometimes for being Polish (when in Paris a piece of
clothing, and ermine wrap, is stolen from their room, the police responds “What
can you expect – only Poles live there!”), sometimes because he did not believe
in God, and sometimes because his wife was Russian and very emancipated (living
independently in Paris as an artist). He was fiercely against discrimination.
In 1933, as anti-Semitism is on the rise among students at the university where
he works in Poland, he complains to Egon Pearson in a letter that the students are behaving with Jews as Americans do with people of color. In 1941 at
UC Berkeley he hired women at a time it was not easy for a woman to get a job
in mathematics. <span style="mso-spacerun: yes;"> </span></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In 1942,
Neyman examined the possibility of hiring David Blackwell, a Black
statistician, then still a student. Neyman met him in New York (so that
Blackwell does not need to travel to Berkeley at his own expense) and considered
Blackwell the best candidate for the job. The wife of a mathematics professor
(who was born in the south of the US) learned about the possibility that a
Black statistician might be hired, warns she will not invite a Black man to her
house, and there was enough concern for the effect the hire would have on the
department that Neyman can not make an offer to Blackwell. He is able to get
Blackwell to Berkeley in 1953 as a visiting professor, and offers him a tenured
job in 1954, making David Blackwell the first tenured faculty member at the
University of Berkeley, California. And Neyman did this, even though Blackwell
was a Bayesian<a href="#_edn5" name="_ednref5" style="mso-endnote-id: edn5;" title=""><span class="MsoEndnoteReference"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="EN-US" style="font-size: 11pt; line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[v]</span></span></span></span></a>
;). </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In 1963,
Neyman travelled to the south of the US and for the first time directly
experienced the segregation. Back in Berkeley, a letter is written with a
request for contributions for the Southern Christian Leadership Conference (founded
by Martin Luther King, Jr. and others), and 4000 copies are printed and shared
with colleagues at the university and friends around the country, which brought
in more than $3000. He wrote a letter to his friend Harald Cramér that he
believed Martin Luther King, Jr. deserved a Nobel Peace Prize (which Cramér
forwarded to the chairman of the Nobel Committee, and which he believed might
have contributed at least a tiny bit to fact that Martin Luther King, Jr. was awarded
the Nobel Prize a year later). Neyman also worked towards the establishment of
a Special Scholarships Committee at UC Berkeley with the goal of providing
education opportunities to disadvantaged Americans </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Neyman
was not a pacifist. In the second world war he actively looked for ways he
could contribute to the war effort. He is involved in statistical models that
compute the optimal spacing of bombs by planes to clear a path across a beach
of land mines. (When at a certain moment he needs specifics about the beach, a
representative from the military who is not allowed to directly provide this
information asks if Neyman has ever been to the seashore in France, to which
Neyman replies he has been to Normandy, and the representative answers “Then
use that beach!”). But </span><span lang="EN-US" style="mso-ansi-language: EN-US;"><span lang="EN-US" style="mso-ansi-language: EN-US;">Neyman
early and actively opposed the Vietnam war, despite the risk of losing
lucrative contracts the Statistical Laboratory had with the Department of Defense. </span>In 1964 he joined a group of people who bought advertisements
in local newspapers with a picture of a napalmed Vietnamese child with the
quote “The American people will bluntly and plainly call it murder”.</span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;"><br /></span></b></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">A positive
role model</span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><br /></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">It is
important to know the history of a scientific discipline. Histories are
complex, and we should resist overly simplistic narratives. If your teacher
explains frequentist statistics to you, it is good if they highlight
that someone like Fisher had questionable ideas about eugenics. But the early
developments in frequentist statistics involved many researchers beyond Fisher</span><span lang="EN-US" style="mso-ansi-language: EN-US;"><span lang="EN-US" style="mso-ansi-language: EN-US;"><a href="#_edn6" name="_ednref6" style="mso-endnote-id: edn6;" title=""><span class="MsoEndnoteReference"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="EN-US" style="font-size: 11pt; line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[vi]</span></span></span></span></a></span>,
and, luckily, there are many more positive role-models that also deserve to be mentioned - such as Jerzy Neyman.
Even though Neyman’s philosophy on statistical inferences forms the basis of how
many scientists nowadays test hypotheses, his contributions and personal life
are still often not discussed in histories of statistics - an oversight I hope the current blog post can somewhat mitigate. If you
want to learn more about the history of statistics through Neyman’s personal
life, I highly recommend the </span><span lang="NL"><a href="https://www.springer.com/gp/book/9780387983578"><span lang="EN-US" style="mso-ansi-language: EN-US;">biography of Neyman by Constance Reid</span></a></span><span lang="EN-US" style="mso-ansi-language: EN-US;">, which was the source for most of
the content of this blog post. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<div style="mso-element: endnote-list;"><br clear="all" />
<hr align="left" size="1" width="33%" />
<div id="edn1" style="mso-element: endnote;">
<p class="MsoEndnoteText"><a href="#_ednref1" name="_edn1" style="mso-endnote-id: edn1;" title=""><span class="MsoEndnoteReference"><span lang="NL"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="NL" style="font-size: 10pt; line-height: 107%; mso-ansi-language: NL; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[i]</span></span></span></span></span></a><span lang="NL"> </span><span lang="EN-US" style="mso-ansi-language: EN-US;">See Hacking,
1965: “The mature theory of Neyman and Pearson is very nearly the received theory
on testing statistical hypotheses.”</span></p>
</div>
<div id="edn2" style="mso-element: endnote;">
<p class="MsoEndnoteText"><a href="#_ednref2" name="_edn2" style="mso-endnote-id: edn2;" title=""><span class="MsoEndnoteReference"><span lang="NL"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="NL" style="font-size: 10pt; line-height: 107%; mso-ansi-language: NL; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[ii]</span></span></span></span></span></a><span lang="NL"> </span><span lang="EN-US" style="mso-ansi-language: EN-US;">It turns out,
in the biography, that it was not Fisher, but A. C. Aitken, who reviewed the
paper positively. </span></p>
</div>
<div id="edn3" style="mso-element: endnote;">
<p class="MsoEndnoteText"><a href="#_ednref3" name="_edn3" style="mso-endnote-id: edn3;" title=""><span class="MsoEndnoteReference"><span lang="NL"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="NL" style="font-size: 10pt; line-height: 107%; mso-ansi-language: NL; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[iii]</span></span></span></span></span></a><span lang="NL"> </span><span lang="EN-US" style="mso-ansi-language: EN-US;">Clayton’s book
seems to be mainly intended as an attempt to persuade readers to become a Bayesian,
and not as an accurate analysis of the development of frequentist statistics. </span></p>
</div>
<div id="edn4" style="mso-element: endnote;">
<p class="MsoEndnoteText"><a href="#_ednref4" name="_edn4" style="mso-endnote-id: edn4;" title=""><span class="MsoEndnoteReference"><span lang="NL"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="NL" style="font-size: 10pt; line-height: 107%; mso-ansi-language: NL; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[iv]</span></span></span></span></span></a><span lang="NL"> William Gosset (or 'Student', from 'Student's <i>t</i>-test'), who was the main inspiration for the work by Neyman
and Pearson, is another giant in frequentist statistics who does not in any
way fit into the narrative that frequentist statistics is tied to eugenics, as
his statistical work was motivated by applied research questions in the
Guinness brewery. Gosset was a modest man – which is probably why he rarely
receives the credit he is due.</span></p>
</div>
<div id="edn5" style="mso-element: endnote;">
<p class="MsoEndnoteText"><a href="#_ednref5" name="_edn5" style="mso-endnote-id: edn5;" title=""><span class="MsoEndnoteReference"><span lang="NL"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="NL" style="font-size: 10pt; line-height: 107%; mso-ansi-language: NL; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[v]</span></span></span></span></span></a><span lang="NL"> </span><span lang="EN-US" style="mso-ansi-language: EN-US;">When asked
about his attitude towards Bayesian statistics in 1979, he answered: “It does
not interest me. I am interested in frequencies.” He did note multiple
legitimate approaches to statistics exist, and the choice one makes is largely
a matter of personal taste. Neyman opposed subjective Bayesian statistics
because their use could lead to bad decision procedures, but was very
positive about later work by Wald, which inspired Bayesian statistical decision
theory.</span></p><p class="MsoEndnoteText"><span lang="EN-US" style="mso-ansi-language: EN-US;"><a href="#_ednref6" name="_edn6" style="mso-endnote-id: edn6;" title=""><span class="MsoEndnoteReference"><span lang="NL"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="NL" style="font-size: 10pt; line-height: 107%; mso-ansi-language: NL; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;">[vi]</span></span></span></span></span></a><span class="MsoEndnoteReference"><span lang="NL"><span style="mso-special-character: footnote;"><span class="MsoEndnoteReference"><span face=""Arial",sans-serif" lang="NL" style="font-size: 10pt; line-height: 107%; mso-ansi-language: NL; mso-bidi-font-family: "Times New Roman"; mso-bidi-language: AR-SA; mso-bidi-theme-font: minor-bidi; mso-fareast-font-family: Calibri; mso-fareast-language: EN-US; mso-fareast-theme-font: minor-latin;"> <span style="font-size: small;">For a more nuanced summary of Fisher's life, see <a href="https://www.nature.com/articles/s41437-020-00394-6 " target="_blank">https://www.nature.com/articles/s41437-020-00394-6 </a></span></span></span></span></span></span> <br /></span></p>
</div>
</div>
<p> </p>Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com3tag:blogger.com,1999:blog-987850932434001559.post-53403959632911178072021-08-18T16:10:00.004+02:002021-08-18T16:10:53.841+02:00P-values vs. Bayes Factors<p class="MsoNormal"><i><span lang="EN-US">In the first partially in person scientific
meeting I am attending after the COVID-19 pandemic, the <a href="https://www.lorentzcenter.nl/perspectives-on-scientific-error-parsing-history-and-comparing-viewpoints.html">Perspectives
on Scientific Error conference in the Lorentz Center</a> in Leiden, the
organizers asked Eric-Jan Wagenmakers and myself to engage in a discussion about
p-values and Bayes factors. We each gave 15 minute presentations to set up our
arguments, centered around 3 questions: What is the goal of statistical
inference, What is the advantage of your approach in a practical/applied
context, and when do you think the other approach may be applicable?</span></i></p><i>
</i><p class="MsoNormal"><span lang="EN-US"><span style="mso-spacerun: yes;"> </span></span></p>
<p class="MsoNormal"><b><span lang="EN-US">What is the goal of statistical
inference?</span></b></p>
<p class="MsoNormal"><span lang="EN-US"> </span></p>
<p class="MsoNormal"><span lang="EN-US">When browsing through the latest issue of
Psychological Science, many of the titles of scientific articles make
scientific claims. “Parents Fine-Tune Their Speech to Children’s Vocabulary
Knowledge”, “Asymmetric Hedonic Contrast: Pain is More Contrast Dependent Than
Pleasure”, “Beyond the Shape of Things: Infants Can Be Taught to Generalize
Nouns by Objects’ Functions”, “The Bilingual Advantage in Children’s Executive
Functioning is Not Related to Language Status: A Meta-Analysis”, or “Response
Bias Reflects Individual Differences in Sensory Encoding”. These authors are
telling you that if you take away one thing from the work the have been doing,
it is a claim that some statistical relationship is present or absent. This
approach to science, where researchers collect data to make scientific claims,
is extremely common (we discuss this extensively in our preprint “The Epistemic
and Pragmatic Function of Dichotomous Claims Based on Statistical Hypothesis
Tests” by Uygun- Tunç, Tunç, & Lakens, <a href="https://psyarxiv.com/af9by/">https://psyarxiv.com/af9by/</a>).
It is not the only way to do science – there is purely descriptive work, or
estimation, where researchers present data without making any claims beyond the
observed data, so there is never a single goal in statistical inferences – but
if you browse through scientific journals, you will see that a large percentage
of published articles have the goal to make one or more scientific claims. </span></p>
<p class="MsoNormal"><span lang="EN-US"> </span></p>
<p class="MsoNormal"><span lang="EN-US">Claims can be correct or wrong. If
scientists used a coin flip as their preferred methodological approach to make
scientific claims, they would be right and wrong 50% of the time. This error
rate is considered too high to make scientific claims useful, and therefore
scientists have developed somewhat more advanced methodological approaches to
make claims. One such approach, widely used across scientific fields, is
Neyman-Pearson hypothesis testing. If you have performed a statistical power
analysis when designing a study, and if you think it would be problematic to
p-hack when analyzing the data from your study, you engaged in Neyman-Pearson
hypothesis testing. The goal of Neyman-Pearson hypothesis testing is to control
the maximum number of incorrect scientific claims the scientific community
collectively makes. For example, when authors write “The Bilingual Advantage in
Children’s Executive Functioning is Not Related to Language Status: A
Meta-Analysis” we could expect a study design where people specified a smallest
effect size of interest, and statistically reject the presence of any
worthwhile effect of bilingual advantage in children on executive functioning
based on language status in an equivalence test. They would make such a claim with
a pre-specified maximum Type 1 error rate, or the alpha level, often set to 5%.
Formally, authors are saying “We might be wrong, but we claim there is no
meaningful effect here, and if all scientists collectively act as if we are
correct about claims generated by this methodological procedure, we would be
misled no more than alpha% of the time, which we deem acceptable, so let’s for
the foreseeable future (until new data emerges that proves us wrong) assume our
claim is correct”. Discussion sections are often less formal, and researchers
often violate the code of conduct for research integrity by selectively
publishing only those results that confirm their predictions, which messes up
many of the statistical conclusions we draw in science.</span></p>
<p class="MsoNormal"><span lang="EN-US"> </span></p>
<p class="MsoNormal"><span lang="EN-US">The process of claim making described above
does not depend on an individual’s personal beliefs, unlike some Bayesian
approaches. As Taper and Lele (2011) write: “It is not that we believe that
Bayes’ rule or Bayesian mathematics is flawed, but that from the axiomatic
foundational definition of probability Bayesianism is doomed to answer
questions irrelevant to science. We do not care what you believe, we barely
care what we believe, what we are interested in is what you can show.” This
view is strongly based on the idea that the goal of statistical inference is
the accumulation of correct scientific claims through methodological procedures
that lead to the same claims by all scientists who evaluate the tests of these
claims. Incorporating individual priors into statistical inferences, and making
claims dependent on their prior belief, does not provide science with a
methodological procedure that generates collectively established scientific
claims. Bayes factors provide a useful and coherent approach to update
individual beliefs, but they are not a useful tool to establish collectively
agreed upon scientific claims. </span></p>
<p class="MsoNormal"><span lang="EN-US"> </span></p>
<p class="MsoNormal"><b><span lang="EN-US">What is the advantage of your approach
in a practical/applied context? </span></b></p>
<p class="MsoNormal"><span lang="EN-US"> </span></p>
<p class="MsoNormal"><span lang="EN-US">A methodological procedure built around a
Neyman-Pearson perspective works well in a science where scientists want to
make claims, but we want to prevent too many incorrect scientific claims. One
attractive property of this methodological approach to make scientific claims is
that the scientific community can collectively agree upon the severity with
which a claim has been tested. If we design a study with 99.9% power for the
smallest effect size of interest and use a 0.1% alpha level, everyone agrees
the risk of an erroneous claim is low. If you personally do not like the claim,
several options for criticism are possible. First, you can argue that no matter
how small the error rate was, errors still <span style="mso-spacerun: yes;"> </span>occur with their appropriate frequency, no
matter how surprised we would be if they occur to us (I am paraphrasing Fisher).
Thus, you might want to run two or three replications, until the probability of
an error has become too small for the scientific community to consider it
sensible to perform additional replication studies based on a cost-benefit
analysis. Because it is practically very difficult to reach agreement on cost-benefit
analyses, the field often resorts to rules or regulations. Just like we can
debate if it is sensible to allow people to drive 138 kilometers per hour on
some stretches of road at some time of the day if they have a certain level of
driving experience, such discussions are currently too complex to practically
implement, and instead, thresholds of 50, 80, 100, and 130<span style="mso-spacerun: yes;"> </span>are used (depending on location and time of
day). Similarly, scientific organizations decide upon thresholds that certain
subfields are expected to use (such as an alpha level of 0.000003 in physics to
declare a discovery, or the 2 study rule of the FDA). </span></p>
<p class="MsoNormal"><span lang="EN-US"> </span></p>
<p class="MsoNormal"><span lang="EN-US">Subjective Bayesian approaches can be used
in practice to make scientific claims. For example, one can preregister that a
claim will be made when a BF > 10 and smaller than 0.1. This is done in
practice, for example in Registered Reports in Nature Human Behavior. The
problem is that this methodological procedure does not in itself control the
rate of erroneous claims. Some researchers have published frequentist analyses
of Bayesian methodological decision rules (Note: Leonard Held brought up these
Bayesian/Frequentist compromise methods as well – during coffee after our
discussion, EJ and I agreed that we like those approaches, as they allow
researcher to control frequentist errors, while interpreting the evidential
value in the data – it is a win-won solution). This works by determining
through simulations which test statistic should be used as a cut-off value to
make claims. The process is often a bit laborious, but if you have the
expertise and care about evidential interpretations of data, do it. </span></p>
<p class="MsoNormal"><span lang="EN-US"> </span></p>
<p class="MsoNormal"><span lang="EN-US">In practice, an advantage of frequentist
approaches is that criticism has to focus on data and the experimental design,
which can be resolved in additional experiments. In subjective Bayesian
approaches, researchers can ignore the data and the experimental design, and
instead waste time criticizing priors. For example, in a comment on Bem (2011)
Wagenmakers and colleagues concluded that “We reanalyze Bem’s data with a
default Bayesian t test and show that the evidence for psi is weak to
nonexistent.” In a response, Bem, Utts, and Johnson stated “We argue that they
have incorrectly selected an unrealistic prior distribution for their analysis
and that a Bayesian analysis using a more reasonable distribution yields strong
evidence in favor of the psi hypothesis.” I strongly expect that most
reasonable people would agree more strongly with the prior chosen by Bem and
colleagues, than the prior chosen by Wagenmakers and colleagues (Note: In the
discussion EJ agreed he in hindsight did not believe the prior in the main
paper was the best choice, but noted the supplementary files included a sensitivity
analysis that demonstrated the conclusions were robust across a range of priors,
and that the analysis by Bem et al combined Bayes factors in a flawed approach).
More productively than discussing priors, data collected in direct replications
since 2011 consistently lead to claims that there is no precognition effect. As
Bem has not been able to succesfully counter the claims based on data collected
in these replication studies, we can currently collectively as if Bem’s studies
were all Type 1 errors (in part caused due to extensive p-hacking). </span></p>
<p class="MsoNormal"><span lang="EN-US"> </span></p>
<p class="MsoNormal"><b><span lang="EN-US">When do you think the other approach may
be applicable?</span></b></p>
<p class="MsoNormal"><b><span lang="EN-US"> </span></b></p>
<p class="MsoNormal"><span lang="EN-US">Even when, in the approach the science I
have described here, Bayesian approaches based on individual beliefs are not
useful to make collectively agreed upon scientific claims, all scientists are
Bayesians. First, we have to rely on our beliefs when we can not collect sufficient
data to repeatedly test a prediction. When data is scarce, we can’t use a
methodological procedure that makes claims with low error rates. Second, we can
benefit from prior information when we know we can not be wrong. Incorrect
priors can mislead, but if we know our priors are correct, even though this
might be rare, use them. Finally, use individual beliefs when you are not
interested in convincing others, but when you only want guide individual
actions where being right or wrong does not impact others. For example, you can
use your personal beliefs when you decide which study to run next.</span></p>
<p class="MsoNormal"><span lang="EN-US"> </span></p>
<p class="MsoNormal"><b><span lang="EN-US">Conclusion</span></b></p>
<p class="MsoNormal"><span lang="EN-US"> </span></p>
<p class="MsoNormal"><span lang="EN-US">In practice, analyses based on p-values and
Bayes factors will often agree. Indeed, one of the points of discussion in the
rest of the day was how we have bigger problems than the choice between
statistical paradigms. A study with a flawed sample size justification or a bad
measure is flawed, regardless of how we analyze the data. Yet, a good
understanding of the value of the frequentist paradigm is important to be able
to push back to problematic developments, such as researchers or journals who
ignore the error rates of their claims, leading to rates of scientific claims
that are incorrect too often. Furthermore, a discussion of this topic helps us
think about whether we actually want to pursue the goals that our statistical
tools achieve, and whether we actually want to organize knowledge generation by
making scientific claims that others have to accept or criticize (a point we
develop further in Uygun- Tunç, Tunç, & Lakens, <a href="https://psyarxiv.com/af9by/">https://psyarxiv.com/af9by/</a>). Yes,
discussions about P-Values and Bayes factors might in practice not have the
biggest impact on improving our science, but it is still important and
enjoyable to discuss these fundamental questions, and I’d like the thank EJ
Wagenmakers and the audience for an extremely pleasant discussion. </span></p>
Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com2tag:blogger.com,1999:blog-987850932434001559.post-66334310503973723782021-05-26T10:11:00.002+02:002021-05-26T19:47:59.939+02:00Can joy and rigor co-exist in science?<p class="MsoNormal"><!--[if !mso]>
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<![endif]--><!--[if gte mso 9]><xml>
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</xml><![endif]--><i>This is a post-publication peer review <span lang="EN-US" style="mso-ansi-language: EN-US;">of "</span></i><span lang="EN-US" style="mso-ansi-language: EN-US;"><span lang="EN-US" style="mso-ansi-language: EN-US;">J</span><span lang="EN-US" style="mso-ansi-language: EN-US;">oy and rigor in behavioral science</span><i><span lang="EN-US" style="mso-ansi-language: EN-US;">". A response by the corresponding author, Leslie John, is at the bottom of this post - make sure to read this as well. </span></i> <br /></span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In a recent
paper “Joy and rigor in behavioral science” </span><span lang="NL"><a href="https://doi.org/10.1016/j.obhdp.2021.03.002"><span lang="EN-US" style="mso-ansi-language: EN-US;">https://doi.org/10.1016/j.obhdp.2021.03.002</span></a></span><span lang="EN-US" style="mso-ansi-language: EN-US;"> Hanne Collins, Ashley Whillans, and
Leslie John aim to examine the behavioral and subjective consequences of
performing confirmatory research (e.g., a preregistered study). In their
abstract they conclude from Study 1 that “engaging in a pre-registration task
impeded the discovery of an interesting but non-hypothesized result” and from
Study 2 that “relative to confirmatory research, researchers found exploratory
research more enjoyable, motivating, and interesting; and less
anxiety-inducing, frustrating, boring, and scientific.” An enjoyable talk about
this paper is available at: </span><span lang="NL"><a href="https://www.youtube.com/watch?v=y31G63iw2xw"><span lang="EN-US" style="mso-ansi-language: EN-US;">https://www.youtube.com/watch?v=y31G63iw2xw</span></a></span><span lang="EN-US" style="mso-ansi-language: EN-US;">.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">I like
meta-scientific work that examines the consequences of changes in scientific
practices. It is likely that new initiatives (e.g., preregistration) will have
unintended negative consequences, and describing these consequences will make
it possible to prevent them through, for example, education. I also think it is
important to examine what makes scientists more or less happy in their job
(although in this respect, my prior is very low that topics such as
preregistration explain a lot of variance compared to job uncertainty, stress,
and a lack of work-life balance).</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">However, I
am less confident in the insights this study provides than the authors suggest
in their abstract and conclusion. First, and perhaps somewhat ironically, the
authors base their conclusions from Study 1 on exploratory analyses that I am
willing to bet are a Type 1 error (or maybe a confound), and are not strong
enough to be taken seriously. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In Study 1
researchers are asked to go through a hypothetical research process in a
survey. Researchers collect data on whether people do yoga on a weekly basis,
how happy participants are today, and the gender of participants. Across 3
conditions, the study was preregistered (see the Figure below), preregistered
with a message that they could still explore, and non-preregistered. The
researchers counted how many of 7 possible analysis options were selected
(including an ‘other’ option). The hypothesis is that if researchers explore
more in non-preregistered analyses, they would select more of these 7 analysis
options to perform in the hypothetical research project. </span></p>
<p class="MsoNormal"><span lang="NL" style="mso-no-proof: yes;"><img alt="" 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/><br /></span><span lang="EN-US" style="mso-ansi-language: EN-US;"></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The authors
write their interest is in whether “participants in the confirmation condition
viewed fewer analyses overall and were less likely to view and report the
results of the gender interaction”. This first analysis seems to be a direct
test of a logical prediction. The second prediction is surprising. Why would
researchers care about the results of a gender interaction? It turns out that
this is the analysis where the authors have hidden a significant interaction
that can be discovered through exploring. Of course, the participants do not
know this. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The results
showed the following:</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p><p class="MsoNormal"><i><span lang="EN-US" style="mso-ansi-language: EN-US;">A
negative binomial logistic regression (Hilbe, 2011) revealed no difference
between conditions in the number of analyses participants viewed (Mexploration
= 3.48, SDexploration = 2.08; Mconfirmation = 3.79, SDconfirmation = 1.99;
Mhybrid = 3.67, SDhybrid = 2.19; all ps ≥ 0.45). Of particular interest, we
assessed between condition differences in the propensity to view the results of
an exploratory interaction using binary logistic regressions. In the
confirmation condition, 53% of participants viewed the results of the
interaction compared with 69% in the exploration condition, b = 0.70, SE =
0.24, p = .01.</span></i></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">So the main
result here is clear: The is no effect of confirmatory research on the tendency
to explore. This is the main conclusion from this analysis. Then, the authors
do something very weird. They analyze the item that, unbeknownst to
participants, would have revealed a significant interaction. This is one of 7
options participants could click on. The difference they report (p = 0.01) is
not significant if the authors correct for multiple comparisons [NOTE: The
authors made the preregistration public after I wrote a draft of this blog <a href="https://aspredicted.org/dg9m9.pdf">https://aspredicted.org/dg9m9.pdf</a>
and this reveals they <b>did</b> a-priori plan to analyze this item separately –
it nicely shows how preregistration allows readers to evaluate the severity of
a test (<a href="https://www.jstage.jst.go.jp/article/sjpr/62/3/62_221/_article/-char/en">Lakens,
2019</a>), and this test was statistically more severe than I initially though
before I had access to the preregistration – I left this comment in the final
version of the blog for transparency, and because I think it is a nice
illustration of a benefit of preregistration]. But more importantly, there is <b>no
logic</b> behind only testing this item. It is, from the perspective of
participants, not special at all. They don’t know it will yield a significant
result. Furthermore, why would we only care about exploratory analyses that
yield a significant result? There are many reasons to explore, such as getting
a better understanding of the data.</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">To me, this
study nicely shows a problem with exploration. You might get a significant
result, but you don’t know what it means, and you don’t know if you just fooled
yourself. This might be noise. It might be something about this specific item
(e.g., people realize that due to the CRUD factor, exploring gender
interactions without a clear theory is uninteresting, as there are many
uninteresting reasons you observe a significant effect). We don’t know what
drives the effect on this single item. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The authors
conclude “Study 1 provides an “existence proof” that a focus on confirmation can
impede exploration”. First of all, I would like it if we banned the term
‘existence proof’ following a statistical test. We did not find a black swan
feather, and we didn’t dig up a bone. We observed a <i>p</i>-value in a test
that lacked severity, and we might very well be talking about noise. If you
want to make a strong claim, we know what to do: Follow up on this study, and
show the effect again in a confirmatory test. Results of exploratory analysis
of slightly illogical predictions are not ‘existence proofs’. They are a
pattern that is worth following up on, but that we can not make any strong
claims about as it stands.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">In Study 2
we get some insights into why Study 2 was not a confirmatory study replicating
Study 1: Performing confirmatory studies is quite enjoyable, interesting, and
motivating – but it is slightly less so than exploratory work (see the Figure
below). Furthermore, confirmatory tests are more anxiety inducing. I remember
this feeling very well from when I was a PhD student. We didn’t want to do a
direct replication, because what if that exploratory finding in your previous
study didn’t replicate? Then you could no longer make a strong claim based on
the study you had. Furthermore, doing the same thing again, but better, is
simply less enjoyable, interesting, and motivating. The problem in Study 2 is
not in the questions that were asked, but in the questions that were <b>not</b>
asked. </span></p>
<p class="MsoNormal"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://1.bp.blogspot.com/-EtkYdqsxPXM/YK4C6I-_7QI/AAAAAAAA0ug/CuwN8ru3C6A_jqoJcAbSKXmnMd5dzN1SwCLcBGAsYHQ/s1043/graph.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="505" data-original-width="1043" height="194" src="https://1.bp.blogspot.com/-EtkYdqsxPXM/YK4C6I-_7QI/AAAAAAAA0ug/CuwN8ru3C6A_jqoJcAbSKXmnMd5dzN1SwCLcBGAsYHQ/w400-h194/graph.png" width="400" /></a></div><p></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">For
example, the authors did not ask ‘how enjoyable is exploratory research, when
after you have written up the exploratory finding, someone writes a blogpost
about how that finding does not support the strong claims you have made?’ Yet,
the latter might get a lot more weight in the overall evaluation of the utility
of performing confirmatory and exploratory research. Another relevant question
is ‘How bad would you feel if someone tried to replicate your exploratory
finding, but they failed, and they published an article that demonstrated your
‘existence proof’ was just a fluke’? Another relevant question is ‘How
enjoyable is it to see a preregistered hypothesis support your prediction’ or
‘How enjoyable are the consequences of providing strong support for your claims
for where the paper is published, or how often it is cited, and how seriously
it is taken by academic peers’? The costs and benefits of confirmatory studies
are multi-facetted. We should look not just at the utility of performing the
actions, but at the utility of the consequences. I don’t enjoy doing the
dishes, but I enjoy taking that time to call friends and being able to eat from
a clean plate. A complete evaluation of the joy of confirmatory research needs
to ask questions about all facets that go into the utility function.</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"></span><span lang="EN-US" style="mso-ansi-language: EN-US;">To
conclude, I like articles that examine consequences of changes in scientific
practice, but in this case I felt the conclusion were too far removed from the data.
In the conclusion, the authors write “Like exploration, confirmation is
integral to the research process, yet, more so than exploration, it seems to
spur negative sentiment.” Yet, we could just as easily have concluded from the
data that confirmatory and exploratory research are both enjoyable, given the means
of 5.39 and 5.87 on a 7 point scale, respectively. If anything, I was surprised
by how small the difference in effect size is (a d = 0.33 for how enjoyable
both are). Although the authors do not interpret the size of the effects in
their paper, that was quite a striking conclusion for myself – I would have
predicted the difference in how enjoyable these two types of research were to
perform would have been larger. The authors also conclude that about Study 1
that “researchers who were randomly assigned to preregister a prediction were
less likely to discover an interesting, non-hypothesized result.” I am not sure
this is not just a Type 1 error, as the main analysis yielded no significant
result, and my prior is very low that a manipulation that makes people less
likely to explore, would only make them less likely to explore the item that, unbeknownst
to the participants, would reveal a statistically significant interaction, as I
don’t know which mechanism could cause such a specific effect. Instead of
exploring this in hypothetical research projects in a survey, I would love to
see an analysis of the number of exploratory analyses in published preregistered
studies. I would predict that in real research projects, researchers report all
the possibly interesting significant results they can find in exploratory
analyses.</span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"><span lang="EN-US" style="mso-ansi-language: EN-US;"><!--[if gte mso 9]><xml>
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</span></p><p class="xmsonormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">Leslie
John’s response:</span></b></p>
<p class="xmsonormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<ol start="1" style="margin-top: 0cm;" type="1"><li class="xmsolistparagraph" style="margin-left: 0cm; mso-list: l1 level1 lfo1; tab-stops: list 36.0pt;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-fareast-font-family: "Times New Roman";">Lakens characterizes Study 1 as
exploratory, but it was explicitly a confirmatory study. As specified in <a href="https://eur02.safelinks.protection.outlook.com/?url=https%3A%2F%2Faspredicted.org%2Fdg9m9.pdf&data=04%7C01%7C%7C708a7e092a2240ec157208d91fb0bec0%7Ccc7df24760ce4a0f9d75704cf60efc64%7C1%7C1%7C637575666237897823%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C1000&sdata=AmHrvoH1tmcumQlDg5n6%2BSKLeR%2BAyg%2FN9dENx62ByZM%3D&reserved=0">our
pre-registration</a>, our primary hypothesis was “H1: We will test whether
participants in a confirmatory mindset (vs. control) will be less likely
to seek results outside of those that would confirm their prediction
(i.e., to seek the results of an interaction, as opposed to simply the
predicted main effect).” Lakens is accurate in noting that participants
did not know a priori that the interaction was significant, but this is
precisely our point: when people feel that exploration is off-limits,
informative effects can be missed. Importantly, we also stress that a
researcher shouldn’t simply send a study off for publication once s/he has
discovered something new (like this interaction effect) through
exploration; rather “exploration followed by rigorous confirmation is
integral to scientific discovery” (p. 188). As a result, Lakens’ questions
such as “How bad would you feel if someone tried to replicate your
exploratory finding, but they failed, and they published an article that
demonstrated your ‘existence proof’ was just a fluke” is not an event that
researchers would need to worry about if they follow the guidance we offer
in our paper (which underscores the importance of exploration followed by
confirmation).</span></li></ol>
<p class="xmsolistparagraph"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p>
<ol start="2" style="margin-top: 0cm;" type="1"><li class="xmsolistparagraph" style="margin-left: 0cm; mso-list: l0 level1 lfo2; tab-stops: list 36.0pt;"><span lang="EN-US" style="mso-ansi-language: EN-US; mso-fareast-font-family: "Times New Roman";">Lakens raises additional
research questions stemming from our findings about the subjective
experience of conducting exploratory versus confirmatory work. We welcome
additional research to better understand the subjective experience of
conducting research in the reform era. We suspect that one reason that
research in the reform era can induce anxiety is the fear of
post-publication critiques, and the valid concern that such critiques will
mispresent both one’s findings as well as one’s motives for conducting the
research. We are therefore particularly solicitous of research that speaks
to making the research process, including post-publication critique, not
only rigorous but joyful.</span></li></ol>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span>
</p>Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com12tag:blogger.com,1999:blog-987850932434001559.post-43846037519607962202020-11-29T09:47:00.003+01:002020-11-29T11:01:51.082+01:00Why I care about replication studies<p>In 2009 I attended a European Social Cognition Network meeting in Poland. I only remember one talk from that meeting: A short presentation in a nearly empty room. The presenter was a young PhD student - Stephane Doyen. He discussed two studies where he tried to replicate a well-known finding in social cognition research related to elderly priming, which had shown that people walked more slowly after being subliminally primed with elderly related words, compared to a control condition.<br /><br />His presentation blew my mind. But it wasn’t because the studies failed to replicate – it was widely known in 2009 that these studies couldn’t be replicated. Indeed, around 2007, I had overheard two professors in a corridor discussing the problem that there were studies in the literature everyone knew would not replicate. And they used this exact study on elderly priming as one example. The best solution the two professors came up with to correct the scientific record was to establish an independent committee of experts that would have the explicit task of replicating studies and sharing their conclusions with the rest of the world. To me, this sounded like a great idea.<br /><br />And yet, in this small conference room in Poland, there was this young PhD student, acting as if we didn’t need specially convened institutions of experts to inform the scientific community that a study could not be replicated. He just got up, told us about how he wasn’t able to replicate this study, and sat down.<br /></p><p><br />It was heroic.<br /></p><p><br />If you're struggling to understand why on earth I thought this was <i>heroic</i>, then this post is for you. You might have entered science in a different time. The results of replication studies are no longer communicated only face to face when running into a colleague in the corridor, or at a conference. But I was impressed in 2009. I had never seen anyone give a talk in which the only message was that an original effect didn’t stand up to scrutiny. People sometimes presented successful replications. They presented null effects in lines of research where the absence of an effect was predicted in some (but not all) tests. But I’d never seen a talk where the main conclusion was just: “This doesn’t seem to be a thing”.<br /><br />On 12 September 2011 I sent Stephane Doyen an email. “Did you ever manage to publish some of that work? I wondered what has happened to it.” Honestly, I didn’t really expect that he would manage to publish these studies. After all, I couldn’t remember ever having seen a paper in the literature that was just a replication. So I asked, even though I did not expect he would have been able to publish his findings. <br /><br />Surprisingly enough, he responded that the study would soon appear <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0029081">in press</a>. I wasn’t fully aware of new developments in the publication landscape, where Open Access journals such as PlosOne published articles as long as the work was methodologically solid, and the conclusions followed from the data. I shared this news with colleagues, and many people couldn’t wait to read the paper: An article, in print, reporting the failed replication of a study many people knew to be not replicable. The excitement was not about learning something new. The excitement was about seeing replication studies with a null effect appear in print. <br /><br />Regrettably, not everyone was equally excited. The publication also led to extremely harsh online comments from the original researcher about the expertise of the authors (e.g., suggesting that findings can fail to replicate due to “Incompetent or ill-informed researchers”), and the quality of PlosOne (“which quite obviously does not receive the usual high scientific journal standards of peer-review scrutiny”). This type of response happened again, and again, and again. Another failed replication led to a letter by the original authors that circulated over email among eminent researchers in the area, was addressed to the original authors, and ended with “do yourself, your junior co-authors, and the rest of the scientific community a favor. Retract your paper.”<br /><br />Some of the historical record on discussions between researchers around between 2012-2015 survives online, in Twitter and Facebook discussions, and blogs. But recently, I started to realize that most early career researchers don’t read about the replication crisis through these original materials, but through summaries, which don’t give the same impression as having lived through these times. It was weird to see established researchers argue that people performing replications lacked expertise. That null results were never informative. That thanks to dozens of conceptual replications, the original theoretical point would still hold up even if direct replications failed. As time went by, it became even weirder to see that none of the researchers whose work was not corroborated in replication studies ever published a preregistered replication study to silence the critics. And why were there even two sides to this debate? Although most people agreed there was room for improvement and that replications should play some role in improving psychological science, there was no agreement on how this should work. I remember being surprised that a field was only now thinking about how to perform and interpret replication studies if we had been doing psychological research for more than a century.<br /> </p><p>I wanted to share this autobiographical memory, not just because I am getting old and nostalgic, but also because young researchers are most likely to learn about the replication crisis through summaries and high-level overviews. Summaries of history aren’t very good at communicating how confusing this time was when we lived through it. There was a lot of uncertainty, diversity in opinions, and lack of knowledge. And there were a lot of feelings involved. Most of those things don't make it into written histories. This can make historical developments look cleaner and simpler than they actually were.<br /><br /> It might be difficult to understand why people got so upset about replication studies. After all, we live in a time where it is possible to publish a null result (e.g., in journals that only evaluate methodological rigor, but not novelty, journals that explicitly invite replication studies, and in Registered Reports). Don't get me wrong: We still have a long way to go when it comes to funding, performing, and publishing replication studies, given their important role in establishing regularities, especially in fields that desire a reliable knowledge base. But perceptions about replication studies have changed in the last decade. Today, it is difficult to <i>feel </i>how unimaginable it used to be that researchers in psychology would share their results at a conference or in a scientific journal when they were not able to replicate the work by another researcher. I am sure it sometimes happened. But there was clearly a reason those professors I overheard in 2007 were suggesting to establish an independent committee to perform and publish studies of effects that were widely known to be not replicable.<br /><br />As people started to talk about their experiences trying to replicate the work of others, the floodgates opened, and the shells fell off peoples' eyes. Let me tell you that, from my personal experience, we didn't call it a replication crisis for nothing. All of a sudden, many researchers who thought it was their own fault when they couldn't replicate a finding started to realize this problem was systemic. It didn't help that in those days it was difficult to communicate with people you didn't already know. Twitter (which is most likely the medium through which you learned about this blog post) launched in 2006, but up to 2010 hardly any academics used this platform. Back then, it wasn't easy to get information outside of the published literature. It's difficult to express how it feels when you realize 'it's not me - it's all of us'. Our environment influences which phenotypic traits express themselves. These experiences made me care about replication studies.</p><p> If you started in science when replications were at least somewhat more rewarded, it might be difficult to understand what people were making a fuss about in the past. It's difficult to go back in time, but you can listen to the stories by people who lived through those times. Some highly relevant stories were shared after the recent <a href="https://www.researchgate.net/profile/Martin_Hagger/publication/346303522_A_multi-site_preregistered_paradigmatic_test_of_the_ego_depletion_effect/links/5fbdef9da6fdcc6cc6640568/A-multi-site-preregistered-paradigmatic-test-of-the-ego-depletion-effect.pdf">multi-lab failed replication of ego-depletion</a> (see tweets by <a href="https://twitter.com/tcarpenter216/status/1331666865135919104?s=20">Tom Carpenter</a> and <a href="https://twitter.com/dsquintana/status/1331689196378058755?s=20">Dan Quintana</a>). You can ask any older researcher at your department for similar stories, but do remember that it will be a lot more difficult to hear the stories of the people who left academia because most of their PhD consisted of failures to build on existing work.<br /><br />If you want to try to feel what living through those times must have been like, consider this thought experiment. You attend a conference organized by a scientific society where all society members get to vote on who will be a board member next year. Before the votes are cast, the president of the society informs you that one of the candidates has been disqualified. The reason is that it has come to the society’s attention that this candidate selectively reported results from their research lines: The candidate submitted only those studies for publication that confirmed their predictions, and did not share studies with null results, even though these null results were well designed studies that tested sensible predictions. Most people in the audience, including yourself, were already aware of the fact that this person selectively reported their results. You knew publication bias was problematic from the moment you started to work in science, and the field knew it was problematic for centuries. Yet here you are, in a room at a conference, where this status quo is not accepted. All of a sudden, it feels like it is possible to actually do something about a problem that has made you feel uneasy ever since you started to work in academia.<br /><br />You might live through a time where publication bias is no longer silently accepted as an unavoidable aspect of how scientists work, and if this happens, the field will likely have a very similar discussion as it did when it started to publish failed replication studies. And ten years later, a new generation will have been raised under different scientific norms and practices, where extreme publication bias is a thing of the past. It will be difficult to explain to them why this topic was a big deal a decade ago. But since you’re getting old and nostalgic yourself, you think that it’s useful to remind them, and you just might try to explain it to them in a 2 minute TikTok video.<br /><br /><br /></p><p><i>History merely repeats itself. It has all been done before. Nothing under the sun is truly new.<br /></i>Ecclesiastes 1:9<br /><br /> <br /><br /><span style="font-size: x-small;">Thanks to Farid Anvari, Ruben Arslan, Noah van Dongen, Patrick Forscher, </span><span style="font-size: x-small;"><span style="font-size: x-small;">Peder Isager, </span>Andrea Kis, Max Maier, Anne Scheel, Leonid Tiokhin, and Duygu Uygun for discussing this blog post with me (and in general for providing such a stimulating social and academic environment in times of a pandemic). </span></p>Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com17tag:blogger.com,1999:blog-987850932434001559.post-63945567821985882452020-10-17T11:59:00.002+02:002020-10-17T11:59:19.983+02:00The p-value misconception eradication challenge<p><span lang="EN-US" style="mso-ansi-language: EN-US;"><i>If you have educational material that you think will do a better job at preventing p-value misconceptions than the material in my MOOC, join the p-value misconception eradication challenge by proposing an improvement to my current material in a new A/B test in my MOOC. </i><br /></span></p><p><span lang="EN-US" style="mso-ansi-language: EN-US;">I launched
a massive open online course “Improving your statistical inferences” in October
2016. So far around 47k students have enrolled, and the evaluations suggest it
has been a useful resource for many researchers. The first week focusses on
p-values, what they are, what they aren’t, and how to interpret them. </span>
</p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Arianne
Herrera-Bennet was interested in whether an understanding of <i>p</i>-values was
indeed “impervious to correction” as some statisticians believe (Haller &
Krauss, 2002, p. 1) and collected data on accuracy rates on ‘pop quizzes’
between August 2017 and 2018 to examine if there was any improvement in p-value
misconceptions that are commonly examined in the literature. The questions were
asked at the beginning of the course, after relevant content was taught, and at
the end of the course. As the figure below from the <a href="https://psyarxiv.com/zt3g9/" target="_blank">preprint</a> shows, there was clear improvement, and accuracy rates were quite high for 5
items, and reasonable for 3 items. </span></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://1.bp.blogspot.com/-ZZTpJdN0a9I/X4q-mH5h2FI/AAAAAAAAv-o/Zv097nN1QzwtG4bnSoGyKAY6NFWMtjpIACLcBGAsYHQ/s962/2020-10-17_11-13-28.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="962" data-original-width="911" height="320" src="https://1.bp.blogspot.com/-ZZTpJdN0a9I/X4q-mH5h2FI/AAAAAAAAv-o/Zv097nN1QzwtG4bnSoGyKAY6NFWMtjpIACLcBGAsYHQ/s320/2020-10-17_11-13-28.png" /></a></div><br /> <p></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">We decided
to perform a follow-up from September 2018 where we added an assignment to week
one for half the students in an ongoing A/B test in the MOOC. In this new assignment,
we didn’t just explain what p-values are (as in the first assignment in the
module all students do) but we also tried to specifically explain common misconceptions,
to explain what <i>p</i>-values are not. The manuscript is still in
preparation, but there was additional improvement for at least some
misconceptions. It seems we can develop educational material that prevents
<i>p</i>-value misconceptions – but I am sure more can be done. </span></p><span lang="EN-US" style="mso-ansi-language: EN-US;">In my paper
to appear in Perspectives on Psychological Science on “<a href="https://psyarxiv.com/shm8v/" target="_blank">The practical
alternative to the <i>p</i>-value is the correctly used <i>p</i>-value</a>” I write: </span>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">“Looking at
the deluge of papers published in the last half century that point out how
researchers have consistently misunderstood <i>p-</i>values, I am left to wonder:
Where is the innovative coordinated effort to create world class educational
materials that can freely be used in statistical training to prevent such misunderstandings?
It is nowadays relatively straightforward to create online apps where people
can simulate studies and see the behavior of p values across studies, which can
easily be combined with exercises that fit the knowledge level of bachelor and master
students. The second point I want to make in this article is that a dedicated
attempt to develop evidence based educational material in a cross-disciplinary
team of statisticians, educational scientists, cognitive psychologists, and
designers seems worth the effort if we really believe young scholars should
understand p values. I do not think that the effort statisticians have made to
complain about <i>p-</i>values is matched with a similar effort to improve the way
researchers use p values and hypothesis tests. We really have not tried hard
enough.”</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">If we
honestly feel that misconceptions of <i>p</i>-values are a problem, and there are
early indications that good education material can help, let’s try to do all we
can to eradicate <i>p</i>-value misconceptions from this world. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">We have
collected enough data in the current A/B test. I am convinced the experimental
condition adds some value to people’s understanding of <i>p</i>-values, so I think it
would be best educational practice to stop presenting students with the control
condition. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">However, I
there might be educational material out there that does a much better job than
the educational material I made, to train away misconceptions. So instead of
giving all students my own new assignment, I want to give anyone who thinks
they can do an even better job the opportunity to demonstrate this. If you have
educational material that you think will work even better than my current
material, I will create a new experimental condition that contains your
teaching material. Over time, we can see which materials performs better, and
work towards creating the best educational material to prevent
misunderstandings of p-values we can. </span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">If you are
interested in working on improving <i>p</i>-value education material, take a look at
the <a href="https://surfdrive.surf.nl/files/index.php/s/CCHQPcfKyir5KbG">first
assignment in the module</a> that all students do, and look at the <a href="https://surfdrive.surf.nl/files/index.php/s/1WvqXPQlBXANa2H">new second
assignment</a> I have created to train away misconception (and the <a href="https://surfdrive.surf.nl/files/index.php/s/EbDE4S7LFcAJViP">answers</a>).
Then, create (or adapt) educational material such that the assignment is
similar in length and content. The learning goal should be to train away common
<i>p</i>-value misconceptions – you can focus on any and all you want. If there are
multiple people who are interested, we collectively vote on which material we
should test first (but people are free to combine their efforts, and work
together on one assignment). What I can offer is getting your material in front
of between 300 and 900 students who enroll each week. Not all of them will
start, not all of them will do the assignments, but your material should reach
at least several hundreds of learners a year, of which around 40% has a masters
degree, and 20% has a PhD – so you will be teaching fellow scientists (and
beyond) to improve how they work. <span style="mso-spacerun: yes;"> </span></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">I will
incorporate this new assignment, and make it publicly available on my blog, as
soon as it is done and decided on by all people who expressed interest in
creating high quality teaching material. We can evaluate the performance by
looking at the accuracy rates on test items. I look forward to seeing your
material, and hope this can be a small step towards an increased effort in improving
statistics education. We might have a long way to go to completely eradicate
<i>p</i>-value misconceptions, but we can start. </span></p>
Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com2tag:blogger.com,1999:blog-987850932434001559.post-83995756742157328742020-09-19T16:25:00.003+02:002020-09-19T16:43:56.735+02:00P-hacking and optional stopping have been judged violations of scientific integrity<p class="MsoNormal"><!--[if gte mso 9]><xml>
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</xml><![endif]--><span lang="EN-US" style="mso-ansi-language: EN-US;">On July 28,
2020, the first Dutch academic has been judged to have violated the code of conduct for research
integrity for <i>p</i>-hacking and optional stopping with the aim of improving
the chances of obtaining a statistically significant result. I think this is a
noteworthy event that marks a turning point in the way the scientific research
community interprets research practices that up to a decade ago were widely
practiced. The researcher in question violated scientific integrity in several
other important ways, including withdrawing blood without ethical consent, and
writing grant proposals in which studies and data were presented that had
not been performed and collected. But here, I want to focus on the judgment
about <i>p</i>-hacking and optional stopping. <br /></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">When I studied
at Leiden University from 1998 to 2002 and commuted by train from my hometown
of Rotterdam I would regularly smoke a cigar in the smoking compartment of the train during my commute. If I would
enter a train today and light a cigar, the responses I would get from my fellow
commuters would be markedly different than 20 years ago. They would probably display
moral indignation or call the train conductor who would give me a fine. Times
change. <br /></span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">When the <a href="https://web.archive.org/web/20130112204021/http:/www.tilburguniversity.edu/nl/nieuws-en-agenda/finalreportLevelt.pdf">report</a>
on the fraud case of Diederik Stapel came out, the three committees were surprised by a
research culture that accepted “sloppy science”. But it did not directly refer
to these practices as violations of the code of conduct for research integrity. For example, on page
57 they wrote:</span></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span><b><span lang="EN-US" style="mso-ansi-language: EN-US;">“In the
recommendations, the Committees not only wish to focus on preventing or
reducing fraud, but also on improving the research culture. The European Code
refers to ‘minor misdemeanours’: some data massage, the omission of some
unwelcome observations, ‘favourable’ rounding off or summarizing, etc. This
kind of misconduct is not categorized under the ‘big three’ (fabrication,
falsification, plagiarism) but it is, in principle, equally unacceptable and
may, if not identified or corrected, easily lead to more serious breaches of
standards of integrity.”</span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Compare
this to the <a href="https://lowi.nl/wp-content/uploads/2020/09/LOWI-advies-2020_10.pdf">report</a>
by <a href="https://lowi.nl/">LOWI</a>, the Dutch National Body for Scientific
Integrity, for a researcher at Leiden University who was judged to violate the
code of conduct for research integrity for <i>p</i>-hacking and optional stopping (note
this is my translation from Dutch of the advice on page 17 point IV, and point
V on page 4):</span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">“The
Board has rightly ruled that Petitioner has violated standards of academic
integrity with regard to points 2 to 5 of the complaint.”</span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">With this, LOWI
has judged that the Scientific Integrity Committee of Leiden University (abbreviated
as CWI in Dutch) ruled correctly with respect to the following: </span></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">“According
to the CWI, the applicant also acted in violation of scientific integrity by
incorrectly using statistical methods (<i>p</i>-hacking) by continuously conducting
statistical tests during the course of an experiment and by supplementing the
research population with the aim of improving the chances of obtaining a
statistically significant result.”</span></b></p>
<p class="MsoNormal"></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">As norms
change, what we deemed a misdemeanor before, is now simply classified as a
violation of academic integrity. I am sure this is very upsetting for this
researcher. We’ve seen similar responses in the past years, where single individuals
suffered more than average researcher for behaviors that many others performed
as well. They might feel unfairly singled out. The only difference between this
researcher at Leiden University, and several others who performed identical
behaviors, was that someone in their environment took the 2018 <a href="http://www.vsnu.nl/files/documents/Netherlands%20Code%20of%20Conduct%20for%20Research%20Integrity%202018.pdf">Netherlands
Code of Conduct for Research Integrity</a> seriously when they read section 3.7,
point 56: </span></p>
<p class="MsoNormal"></p>
<p class="MsoNormal"><b><span lang="EN-US" style="mso-ansi-language: EN-US;">Call
attention to other researchers’ non-compliance with the standards as well as
inadequate institutional responses to non-compliance, if there is sufficient
reason for doing so.</span></b></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">When it
comes to smoking, rules in The Netherlands are regulated through laws. You’d
think this would mitigate confusion, insecurity, and negative emotions during a
transition – but that would be wishful thinking. In The Netherlands the whole transition
has been ongoing for close to two decades, from an initial law allowing a
smoke-free working environment in 2004, to a completely smoke-free university campus in
August 2020.</span></p>
<p class="MsoNormal"></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">The code of
conduct for research integrity is not governed by laws, and enforcement of the
code of conduct for research integrity is typically not anyone’s full time job. We can therefore
expect the change to be even more slow than the changes in what we feel is
acceptable behavior when it comes to smoking. But there is notable change over
time. </span></p>
<p class="MsoNormal"></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">We see a
shift from the “big three” types of misconduct (fabrication,
falsification, plagiarism), and somewhat vague language of misdemeanors, that
is “in principle” equally unacceptable, and might lead to more serious breaches
of integrity, to a clear classification of <i>p</i>-hacking and optional stopping as
violations of scientific integrity. Indeed, if you ask me, the ‘bigness’ of plagiarism
pales compared to how publication bias and selective reporting distort
scientific evidence. </span></p>
<p class="MsoNormal"></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">Compare
this to smoking laws in The Netherlands, where early on it was still allowed to
create separate smoking rooms in buildings, while from August 2020 onwards all school
and university terrain (i.e., the entire campus, inside and outside of the buildings)
needs to be a smoke-free environment. Slowly but sure, what is seen as
acceptable changes. </span></p>
<p class="MsoNormal"></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">I do not consider
myself to be an exceptionally big idiot – I would say I am pretty average on
that dimension – but it did not occur to me how stupid it was to enter a
smoke-filled train compartment and light up a cigar during my 30 minute commute
around the year 2000. At home, I regularly smoked a pipe (a gift from my father). I still have it. Just looking at the tar stains now makes me doubt my own sanity.<br /></span></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;"> </span></p><div class="separator" style="clear: both; text-align: center;"><span lang="EN-US" style="mso-ansi-language: EN-US;"><a href="https://1.bp.blogspot.com/-IgY2B6EX4jM/X2YP9FBze7I/AAAAAAAAvfE/R4ifc3EQIdMEoys7TAskognX_nYN2NuJwCLcBGAsYHQ/s2048/pijp.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1152" data-original-width="2048" src="https://1.bp.blogspot.com/-IgY2B6EX4jM/X2YP9FBze7I/AAAAAAAAvfE/R4ifc3EQIdMEoys7TAskognX_nYN2NuJwCLcBGAsYHQ/s320/pijp.jpg" width="320" /></a></span></div><span lang="EN-US" style="mso-ansi-language: EN-US;"><br /></span><p></p><p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">This is despite that fact that the relation between
smoking and cancer was pretty well established since the 1960’s. Similarly,
when I did my PhD between 2005 and 2009 I was pretty oblivious to the error
rate inflation due to optional stopping, despite that fact that one of the more
important papers on this topic was published by Armitage, McPherson, and Rowe
in <a href="https://www.jstor.org/stable/2343787" target="_blank">1969</a>. I did realize that flexibility in analyzing data could not be good for
the reliability of the findings we reported, but just like when I lit a cigar
in the smoking compartment in the train, I failed to adequately understand how bad
it was.</span></p>
<p class="MsoNormal"></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">When
smoking laws became stricter, there was a lot of discussion in society. One
might even say there was a strong polarization, where on the one hand newspaper
articles appeared that claimed how outlawing smoking in the train was ‘totalitarian’,
while we also had family members who would no longer allow people to smoke inside their house, which led my parents (both smokers) to stop visiting these family
members. Changing norms leads to conflict. People feel personally attacked,
they become uncertain, and in the discussions that follow we will see all
opinions ranging from how people should be free to do what they want, to how people
who smoke should pay more for healthcare. </span></p>
<p class="MsoNormal"></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">We’ve seen
the same in scientific reform, although the discussion is more often along the
lines of how smoking can’t be that bad if my 95 year old grandmother has been smoking a packet a day for
70 years and feels perfectly fine, to how alcohol use or lack of exercise are
much bigger problems and why isn’t anyone talking about those. </span></p>
<p class="MsoNormal"></p>
<p class="MsoNormal"><span lang="EN-US" style="mso-ansi-language: EN-US;">But
throughout all this discussion, norms just change. Even my parents stopped smoking
inside their own home around a decade ago. The Dutch National Body for
Scientific Integrity has classified <i>p</i>-hacking and optional stopping as
violations of research integrity. Science is continuously improving, but change is slow. Someone once explained to me that correcting the course of science is like steering an oil tanker - any change in direction takes a while to be noticed. But when change happens, it’s worth standing still to
reflect on it, and look at how far we’ve come.<br /></span></p>
Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com3tag:blogger.com,1999:blog-987850932434001559.post-26511101433172535672020-08-05T10:56:00.009+02:002021-01-04T18:19:06.500+01:00Feasibility Sample Size Justification<div><i>This blog post is now included in the paper "Sample size justification" available at <a href="https://psyarxiv.com/9d3yf/">PsyArXiv.</a></i></div><div><i> </i><!--[if !mso]>
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number 5"/>
<w:LsdException Locked="false" Priority="0" QFormat="true" Name="Title"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Closing"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Signature"/>
<w:LsdException Locked="false" Priority="1" SemiHidden="true"
UnhideWhenUsed="true" Name="Default Paragraph Font"/>
<w:LsdException Locked="false" Priority="0" SemiHidden="true"
UnhideWhenUsed="true" QFormat="true" Name="Body Text"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text Indent"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Continue"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Continue 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Continue 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Continue 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Continue 5"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Message Header"/>
<w:LsdException Locked="false" Priority="11" QFormat="true" Name="Subtitle"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Salutation"/>
<w:LsdException Locked="false" Priority="0" SemiHidden="true"
UnhideWhenUsed="true" QFormat="true" Name="Date"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text First Indent"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text First Indent 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Note Heading"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text Indent 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Body Text Indent 3"/>
<w:LsdException Locked="false" Priority="9" SemiHidden="true"
UnhideWhenUsed="true" QFormat="true" Name="Block Text"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Hyperlink"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="FollowedHyperlink"/>
<w:LsdException Locked="false" Priority="22" QFormat="true" Name="Strong"/>
<w:LsdException Locked="false" Priority="20" QFormat="true" Name="Emphasis"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Document Map"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Plain Text"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="E-mail Signature"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Top of Form"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Bottom of Form"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Normal (Web)"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Acronym"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Address"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Cite"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Code"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Definition"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Keyboard"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Preformatted"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Sample"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Typewriter"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Variable"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Normal Table"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="annotation subject"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="No List"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Outline List 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Outline List 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Outline List 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Simple 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Simple 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Simple 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Classic 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Classic 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Classic 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Classic 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Colorful 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Colorful 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Colorful 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Columns 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Columns 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Columns 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Columns 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Columns 5"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 5"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 6"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 7"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Grid 8"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 4"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 5"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 6"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 7"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table List 8"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table 3D effects 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table 3D effects 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table 3D effects 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Contemporary"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Elegant"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Professional"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Subtle 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Subtle 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Web 1"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Web 2"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Web 3"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Balloon Text"/>
<w:LsdException Locked="false" Priority="39" Name="Table Grid"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="Table Theme"/>
<w:LsdException Locked="false" SemiHidden="true" Name="Placeholder Text"/>
<w:LsdException Locked="false" Priority="1" QFormat="true" Name="No Spacing"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading"/>
<w:LsdException Locked="false" Priority="61" Name="Light List"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 1"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 1"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 1"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 1"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 1"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 1"/>
<w:LsdException Locked="false" SemiHidden="true" Name="Revision"/>
<w:LsdException Locked="false" Priority="34" QFormat="true"
Name="List Paragraph"/>
<w:LsdException Locked="false" Priority="29" QFormat="true" Name="Quote"/>
<w:LsdException Locked="false" Priority="30" QFormat="true"
Name="Intense Quote"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 1"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 1"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 1"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 1"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 1"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 1"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 1"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 1"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 2"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 2"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 2"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 2"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 2"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 2"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 2"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 2"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 2"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 2"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 2"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 2"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 2"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 2"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 3"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 3"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 3"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 3"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 3"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 3"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 3"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 3"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 3"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 3"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 3"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 3"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 3"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 3"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 4"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 4"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 4"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 4"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 4"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 4"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 4"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 4"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 4"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 4"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 4"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 4"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 4"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 4"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 5"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 5"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 5"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 5"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 5"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 5"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 5"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 5"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 5"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 5"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 5"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 5"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 5"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 5"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 6"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 6"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 6"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 6"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 6"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 6"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 6"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 6"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 6"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 6"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 6"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 6"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 6"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 6"/>
<w:LsdException Locked="false" Priority="19" QFormat="true"
Name="Subtle Emphasis"/>
<w:LsdException Locked="false" Priority="21" QFormat="true"
Name="Intense Emphasis"/>
<w:LsdException Locked="false" Priority="31" QFormat="true"
Name="Subtle Reference"/>
<w:LsdException Locked="false" Priority="32" QFormat="true"
Name="Intense Reference"/>
<w:LsdException Locked="false" Priority="33" QFormat="true" Name="Book Title"/>
<w:LsdException Locked="false" Priority="37" SemiHidden="true"
UnhideWhenUsed="true" Name="Bibliography"/>
<w:LsdException Locked="false" Priority="39" SemiHidden="true"
UnhideWhenUsed="true" QFormat="true" Name="TOC Heading"/>
<w:LsdException Locked="false" Priority="41" Name="Plain Table 1"/>
<w:LsdException Locked="false" Priority="42" Name="Plain Table 2"/>
<w:LsdException Locked="false" Priority="43" Name="Plain Table 3"/>
<w:LsdException Locked="false" Priority="44" Name="Plain Table 4"/>
<w:LsdException Locked="false" Priority="45" Name="Plain Table 5"/>
<w:LsdException Locked="false" Priority="40" Name="Grid Table Light"/>
<w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark"/>
<w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful"/>
<w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 1"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 1"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 1"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 1"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 1"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 1"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 1"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 2"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 2"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 2"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 2"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 2"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 2"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 2"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 3"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 3"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 3"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 3"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 3"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 3"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 3"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 4"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 4"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 4"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 4"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 4"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 4"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 4"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 5"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 5"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 5"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 5"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 5"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 5"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 5"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 6"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 6"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 6"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 6"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 6"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 6"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 6"/>
<w:LsdException Locked="false" Priority="46" Name="List Table 1 Light"/>
<w:LsdException Locked="false" Priority="47" Name="List Table 2"/>
<w:LsdException Locked="false" Priority="48" Name="List Table 3"/>
<w:LsdException Locked="false" Priority="49" Name="List Table 4"/>
<w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark"/>
<w:LsdException Locked="false" Priority="51" Name="List Table 6 Colorful"/>
<w:LsdException Locked="false" Priority="52" Name="List Table 7 Colorful"/>
<w:LsdException Locked="false" Priority="46"
Name="List Table 1 Light Accent 1"/>
<w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 1"/>
<w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 1"/>
<w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 1"/>
<w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 1"/>
<w:LsdException Locked="false" Priority="51"
Name="List Table 6 Colorful Accent 1"/>
<w:LsdException Locked="false" Priority="52"
Name="List Table 7 Colorful Accent 1"/>
<w:LsdException Locked="false" Priority="46"
Name="List Table 1 Light Accent 2"/>
<w:LsdException Locked="false" Priority="47" Name="List Table 2 Accent 2"/>
<w:LsdException Locked="false" Priority="48" Name="List Table 3 Accent 2"/>
<w:LsdException Locked="false" Priority="49" Name="List Table 4 Accent 2"/>
<w:LsdException Locked="false" Priority="50" Name="List Table 5 Dark Accent 2"/>
<w:LsdException Locked="false" Priority="51"
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<![endif]--><span lang=""> </span></div><div><span lang="">When you perform an experiment, you
want it to provide an answer to your research question that is as informative
as possible. However, since all scientists are faced with resource limitations,
you need to balance the cost of collecting each additional datapoint against
the increase in information that datapoint provides. In economics, this is
known as the Value of Information (Eckermann et al., 2010). Calculating the
value of information is notoriously difficult. You need to specify the costs
and benefits of possible outcomes of the study, and quantifying a utility
function for scientific research is not easy.</span><span lang=""><br /></span></div><div><span lang=""><br /></span></div><div><span lang="">Because of the difficulty of quantifying
the value of information, scientists use less formal approaches to justify the
amount of data they set out to collect. That is, if they provide a
justification for the number of observations to begin with. Even though in some
fields a justification for the number of observations is required when
submitting a grant proposal to a science funder, a research proposal to an
ethical review board, or a manuscript for submission to a journal. In some
research fields, the number of observations is <i style="mso-bidi-font-style: normal;">stated</i>, but not <i style="mso-bidi-font-style: normal;">justified</i>.
This makes it difficult to evaluate how informative the study was. Referees
can’t just assume the number of observations is sufficient to provide an
informative answer to your research question, so leaving out a justification
for the number of observations is not best practice, and a reason reviewers can
criticize your submitted article.</span><a name="feasibility"><span lang=""><br /></span></a></div><div><a name="feasibility"><span lang=""><br /></span></a></div><div><h2><a name="feasibility"><span lang="">Feasibility</span></a><span lang=""></span></h2></div>
<p class="FirstParagraph"><span lang="">A common reason why a specific number
of observations is collected is because collecting more data was not feasible.
Note that all decisions for the sample size we collect in a study are based on
the resources we have available in some way. A <b style="mso-bidi-font-weight: normal;">feasibility justification</b> makes these resource limitations the
primary reason for the sample size that is collected. Because we always have
resource limitations in science, even when feasibility is not our primary
justification for the number of observations we plan to collect, feasibility is
always at least a secondary reason for any sample size justification. Despite
the omnipresence of resource limitations, the topic often receives very little
attention in texts on experimental design. This might make it feel like a feasibility
justification is not appropriate, and you should perform an a-priori power
analysis or plan for a desired precision instead. But feasibility limitations
play a role in every sample size justification, and therefore regardless of
which justification for the sample size you provide, you will almost always
need to include a feasibility justification as well.</span></p>
<p class="MsoBodyText"><span lang="">Time and money are the two main resource
limitations a scientist faces. Our master students write their thesis in 6
months, and therefore their data collection is necessarily limited in whatever
can be collected in 6 months, minus the time needed to formulate a research
question, design an experiment, analyze the data, and write up the thesis. A
PhD student at our department would have 4 years to complete their thesis, but
is also expected to complete multiple research lines in this time. In addition
to limitations on time, we have limited financial resources. Although nowadays
it is possible to collect data online quickly, if you offer participants a
decent pay (as you should) most researchers do not have the financial means to
collect thousands of datapoints.</span></p>
<p class="MsoBodyText"><b style="mso-bidi-font-weight: normal;"><span lang="">A
feasibility justification puts the limited resources at the center of the
justification for the sample size that will be collected</span></b><span lang="">. For example, one might argue that 120 observations is the most
that can be collected in the three weeks a master student has available to
collect data, when each observation takes an hour to collect. A PhD student
might collect data until the end of the academic year, and then needs to write
up the results over the summer to stay on track to complete the thesis in time.</span></p>
<p class="MsoBodyText"><span lang="">A feasibility justification thus <i style="mso-bidi-font-style: normal;">starts</i> with the expected number of
observations (N) that a researcher expects to be able to collect. The challenge
is to evaluate whether collecting N observations is worthwhile. The answer
should sometimes be that data collection is <i style="mso-bidi-font-style: normal;">not</i>
worthwhile. For example, assume I plan to manipulate the mood of participants
using funny cartoons and then measure the effect of mood on some dependent
variable - say the amount of money people donate to charity. I should expect an
effect size around d = 0.31 for the mood manipulation
(Joseph et al., 2020), and seems unlikely that the effect on donations
will be larger than the effect size of the manipulation. If I can only collect
mood data from 30 participants in total, how do we decide if this study will be
informative?</span></p>
<h2><a name="Xa19a7889436b9498ccded0d8a761c20579774c4"><span lang="">How
informative is the data that is feasible to collect?</span></a><span lang=""></span></h2>
<p class="FirstParagraph"><span lang="">If we want to evaluate whether the
feasibility limitations make data collection uninformative, we need to think
about what the goal of data collection is. First of all, having data always
provide more knowledge than not having data, so in an absolute sense, all
additional data that is collected is better than not collecting data. However,
in line with the idea that we need to take into account costs and benefits, it
is possible that the cost of data collection outweighs the benefits. To
determine this, one needs to think about what the benefits of having the data
are. The benefits are clearest when we know for certain that someone is going
to make a decision, with or without data. If this is the case, then any data
you collect will reduce the error rates of a well-calibrated decision process,
even if only ever so slightly. In these cases, the value of information might
be positive, as long as the reduction in error rates is more beneficial than
the costs of data collection. If your sample size is limited and you know you
will make a decision anyway, perform a compromise power analysis, where you
balance the error rates, given a specified effect size and sample size.</span></p>
<p class="MsoBodyText"><span lang="">Another way in which a small dataset can
be valuable is if its existence makes it possible to combine several small
datasets into a meta-analysis. This argument in favor of collecting a small
dataset requires 1) that <b style="mso-bidi-font-weight: normal;">you share the
results in a way that a future meta-analyst can find them regardless of the
outcome of the study</b>, and 2) that <b style="mso-bidi-font-weight: normal;">there
is a decent probability that someone will perform a meta-analysis in the future
which inclusion criteria would contain your study</b>, because a sufficient
number of small studies exist. The uncertainty about whether there will ever be
such a meta-analysis should be weighed against the costs of data collection. Will
anyone else collect more data on cognitive performance during bungee jumps, to
complement the 12 data points you can collect?</span></p>
<p class="MsoBodyText"><span lang="">One way to increase the probability of a
future meta-analysis is if you commit to performing this meta-analysis yourself
in the future. For example, you might plan to repeat a study for the next 12
years in a class you teach, with the expectation that a meta-analysis of 360
participants would be sufficient to achieve around 90% power for d = 0.31. If
it is not plausible you will collect all the required data by yourself, you can
attempt to set up a collaboration, where fellow researchers in your field
commit to collecting similar data, with identical measures, over the next
years. If it is not likely sufficient data will emerge over time, we will not
be able to draw informative conclusions from the data, and it might be more
beneficial to not collect the data to begin with, and examine an alternative
research question with a larger effect size instead.</span></p>
<p class="MsoBodyText"><span lang="">Even if you believe over time sufficient
data will emerge, you will most likely compute statistics after collecting a
small sample size. Before embarking on a study where your main justification
for the sample size is based on feasibility, you can expect. I propose that a
feasibility justification for the sample size, in addition to a reflection on
the plausibility that a future meta-analysis will be performed, and/or the need
to make a decision, even with limited data, is always accompanied by three
statistics, detailed in the following three sections.</span></p>
<h3><a name="X9dbbfcb3cf355c3b08b62c6aebc12a6a6b85d70"><span lang="">The
smallest effect size that can be statistically significant</span></a><span lang=""></span></h3>
<p class="FirstParagraph"><span lang="">In Figure @ref(fig:power-effect1) the
distribution of Cohen’s d given 15 participants per group is plotted when the
true effect size is 0 (or the null-hypothesis is true), and when the true
effect size is d = 0.5. The blue area is the Type 2 error rate (the probability
of not finding p < α, when there is a true effect, and α = 0.05). 1- the
Type 2 error is the statistical power of the test, given an assumption about a
true effect size in the population. <b style="mso-bidi-font-weight: normal;">Statistical
power</b> is the probability of a test to yield a statistically significant
result if the alternative hypothesis is true. Power depends on the Type 1 error rate (α), the true
effect size in the population, and the number of observations.</span></p>
<p class="CaptionedFigure"><span lang="" style="mso-no-proof: yes;"><br /></span></p><p class="CaptionedFigure"><span lang="" style="mso-no-proof: yes;"><img alt="" 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rUJu/EJlpcR69Gyd8QPZJEyUrRSWcrkcHxvBVpM2svlQPkKPfDJeQ6fPctVf7m3EPxgLzPaFydHrHJ0yV+HoSvO8MiccAOtsI+omC0hXF8/lS4/l+KnLuNZ/0wj16VPeB5qseH/bCudcrng2mUnzxxGKFRWUCj9Wd0hG855+7HxpZYIYk0LRM7/n59JhZwZqDd2N8sXrqFfjx506NyNMUv3cdZfXqAZu+f2LpGhXC17XurPP86lXbOonDkl2but55Iisqv0/lC2Ecj2QdVJleC6FVnBJt7cBHTx7IvGbOD5/Wv8tf4sN5SA9TW7h5clRexQNoNVF8hJj2ZkzVmKAZ7XWDpxCh07d6NT78HM3XuHx1o5NOK7SFFZQRH6kqW/JCd50jYM9mhCRVfH/cxKtRYz2OdnJNwij9RoQq6yoHszSmdLEuP7JHMtR52FF/BVg8UGGqs/nq2yk6JcbZqVdiW5JCFJNZm4W247HENZYQKr2Y/dM/pTwTVmOWXMlwfXZBJpqo1iwwMAG8cm1iZ7sSJUqlKJopKEJGWnycRrqIng4rpJtKiUi/SxyjtbuW78sfEhb+0XKzc296dkmgxUHnuWEHsdNwL39o3lh3SpKDHoKCFA0M2NdC0gkalgH0bNqBrj/ErjVJq2o7ZzRSOPgH7K6QERyjEaDXh9bSPdSnxHkebruQ+8uLSC1oWcKNF7P2+w4efjSYvMEhmqDWTelk14rVvPihVz6VDfBUnKQYfdIYSr/TmzYTpty6YivVMD+i3byIYr/gS+ucDkn1KSPMt3FKk/Fo9la1lz4hHPg68yrV42svzkzrEQ+cTcPTQXmZLnIGPqwvw4YBQeq1azwnMmfX7KiyQlJf+IQzyIAJvNn9UdCpGpeC9W3VI4hPJbNvcuRZaC7Vl42Z+n1w4yu0MxXJLlpmbvacz76xrPlBEcHV8d19wNGXfwNTog+II7LfJKSMnL0mz4IhZ6r2al9womdf4ZV0nCpe5Utj2VB44uePalWgoJKWleCnecyaJVq1k2oRvVckhIrg0YckARb+OltoHqxd9MbeyEJGXg+9+mMNN7NSu9VzN3fFeqpJOQXFsy4mwwmFQ8u36IWeM6UylbOrJXH8DMDTvYfu2tvPLaYbs6rLy46UnLrNkoMOgw/qZ39BbsvTOtWe41qHUGzi+qirMkkaZoR4YsWMUy79UsXeZOj0rZkKRslJ9zBj81mC0vWdkmH5mcXMmQvSpN+y9iwarVzOjSmHRJJaQmizjlJ4/L+v3Vh8ppJKTCteg0azlLvFez0nsBI5qWJGnq/LRZcoVXBrCg5eCkJpSQJKTUZagyYD5LV69m0bBmFMsoIRXvwcJL8gCfyt64GCxgRMnfM38lbzIXKvWcxarNG1m1bi1L5w6Rgz1PWxbftWKzxVMG9qE8gzGIk/uWMX/JVvb8OY4WjUuRrVJHxu33Q0usBkcPEbGG8t71inl+WXn1yIsWabPiOuAg/mqHULZAhDKczX2y4ZyhAyt9NShihbK8yMuG39Ze5HFOTs6CpcgsOVywpspJ2fpjWH8jnLe2j5xTPjKOatnzUHPSWQLNIewaXY7MTsXou+kZIQBan/eGstas59Hlk2xYuZolCdrKzpO++JvjX4cQOS1kwT48G/6YrcPKxQllpRUMoU/ZOaIcyb5LT4ECJUniGJS5K9O83xrOhhHvWhGVFRRhAazplonUSbOTKV1Gancex0Kv1SxbNoVO5ZyRpPQUn3ae11p5VOvG6vrkliSSVGnFwIVeLPVezXLP6fSumhMp8/eM2vOGt2Yw2AJZ160kWTMlIUP57oybvRqvXZe4FWKwrwmI3g8NFu5uG0LVNBJS/h9pM20pS71XM2/iICpkTIMkSWRuMY3tj+RQPunxO2UzSUgFG9Bj7Cq8Nx7lzAs94Q820PP7VEhSPmr28mC+92o8vb2YPqAZhSWJdGUHsvi2PPh9a+dIqubIS52pF6NC2QA8OORO3dzZqTL6JCFA8J09DP/pO1ImdyJn3sK0GzkfL+/VzJ/al5rOEtJ3lemw1x/TR6xJEaEc6SNCOfj+PoZ8n5R8VRbjA7yMCuWDBGLl3vEJVJRSkaWKO6suviDcPnymDrmC14at/Hk9Ag1g0AXh2Sod2fL/wVF7AxXqf45x5VPxXZqaTLhljpqDVYZew71OVodQNrJrkCuZUjlTo+9hzimiZziDbu6ga9m0SC49Wf9AjZ5g1rwzlNsx/0IYNuChdwOKpKjA0APB9kZVy75R1XDN3YhxBwOwoGT/kOq4SPmp98dhHjm0vJa391ncuSTpk5ak4/IHKAEfz26Uk9JSrNVy9insb7Q+Z13f70mXvAJtPO/FuwjKiIFbO8ZSJU0a8v0ylxNvHFpxayhH53agaLK0FGn3J8/tP1Y82kKnYs6UGvg3YZE/i7VdPSaeXZ5DrWTZKTT+HErDh837qG0QGurDuJKpSJG7MdOPBKBw2KU3VzbSvmRKJNcBbH2uR4s/nq3zklFKS8URx/GxT3Jbgw7RqZAzUqEhbL6vxaq7w8zGOUhT+FemnAyMMU+N9iFzm+Uka9M/2PbYDBg5OKk++SUXKg/dw7nIstf5MLNJAZKnbsio/a/QxypPK2/YPLAWmaXslOu9nZNPgqLm2u5f38fSzUc5/8IW7/yg0gL60IdsWzqcxt0nsfrILQKxEfRkD4NrFyV1oc5MPheM1UrUve1qK4S+ecCWyS1p2uIdWveg5/p78n22xuhQfv3Ym18zZMW1735exwnlUDb2zIZz5s54+cUfyiqDhSNjCpFKkkhSrhHdZy1ikZcXS1fOon+NfEiSRJ4Bf3Ij1Crv9we0D46hXGviUR4Apuer6VQwOd8VHc6qhzqw3WD2e0I5XB93fURCL43+A+a8rYAi/lDWAGG+l3CvJYdw7l/7Mn7pCpat8mL+jCE0ziEhSYVptMEXTTxDxpGh7N0pDWm+y0vDEfu5ERi9f35nvPilUCqk/KM4EKAnIvAUgyqkJUXVvqx7GHP21/LmNCOqpiVXjw1cDAIbIaztVhwnKR+/rn0gX9Rgn0bSxzyeJt1tZjcuQLJM1ei5/irRTYGOm2tGUzFPcpLXmxgdyvNbU0xKT5mBO7kZtYhFy8lZrcmf1IXyfTdxw/HkVb9g47DqZJNyUGe8D2HA3Y8I5SGVJL7LVZPBq67iH7VoI4IzK/vyfeo05Gm+kYcQp4MgQvl9PjCUdcCrq2vpVDQDxdpu4ynw4mJ0T/kVNkIeHWBkhZRI0neky1OeuvWb0rrdYIZN2MmZ12bMgM4GYYEPmd88LS55R7ArwoYWePvmNGN+yIxL+Zmc0shhoLFC2NvYoaxn54CcOKdvzILHRnm+zYB9kYWV8+61yZG8EoMPvSKIUNZ1fE8oXwzFAFxZXJuCKb6n79ZHBAAWNOwfbQ/lwyHAXWbULUCmQv1YdU9eLqGyL24zAP7HJ1HLyYUfBu7lEXBtZQeKZShM64W3UQJaPZix4uPZi+LpCtPE4wqhxBoCNYHVGsBf7k3Ika4SvXaGYnP4HC2g9DvKqArOuJYbxz6V3Gvw9fGiTWEnivfcwzPi7wXpsPLyzjra5nYhf+99vDDKw27x1gmHhSVWrATcWUzD9E4U63WQp0Z5UVuEXu5F6zFycsJPZEtRjZGnggi2BuLVOi+Zcv7GkptqeWGfGXS8wLN5YdLn7cqymyrMir10Ke6ClOMnWnUfwtAB/ejeqx/de/en37DB/PZDDlIW7cy8swps2Dj8Ry1yZK7GsN0v5WOuBxM6jk5oRPZ0P9B3y2OUscpTj477W0dTM6uEJLmSt8yPNGnalg5dxjDd8yKPTfYH38Qzt2/AxMO9E2jzfVW6bX8b9TAUgFen51AjS0oKtNuNn0N5q8wQFvKK4xumMm7iO7gvwOPoC3ku0iGU391TVrDlHT1leRWxjXun17Ns2XLWXXmJQ47w9u4e+lV1IolzO5ZeCUUd+/cTEDOUj3HfCqDg/MIO5E6ZkWqTzxCk92PJL4XeGcp6i5qr+1Yztnc/uidoHDPXXZfnYN+3b+8IZZUZwkIDOL1jCfNWb+HQK4PDQikt17aMpZpTGjJXX8QVnTwv7hgacii/watdBpyydWZtsHw+qA1yL1xvU3FwWEVcUtdl6pW3BNxfRr0sGUhStAnd+w5hYH+5LvfoM4C+Q/vSqGRmklYZx+4nBiyEsq5LUTJna8via+HycYin/iktYH2ynt9KfIfzrys4a0/vCD0oATRnGFErGxmqj2XrI3soz2lO/oxl6O79MGoo3GZ6xMrO5cjs2hL3c/o47Vbo1RW0zZ2ZIi29uA482vWhobyTfqWSkbfmIi4BZmt0G6UOvID7z9lwLjKYbeHx35opQvldPiSUjfIQ4o1tQyicIS8VplzGAPheXEGrQk6U6L2X54Ber+Xe+Q0MGdCc0jHmHtOQt2o7+q+7SRCgCH4UfyhXcyJro8VcCLeitr4rlHPhnL4jK56p5MpngAiTfEX2bGtX8jmXouvm5/gTxvqOhchUqi/etyPkCmMELWFs7V8ap0IfGMpH3gIXGV3DldwN3dnrJ58gkVfzKkB7axVtSuagZNeN3ASur2hPsRxlae91HxXRczPXN/SnQo4iNPW4RljsUDaDzfScbSNrkCNvAyZfiPk5SsAQcI2F7YuQpUx/Nvt/eChrgGDf84z9UcKpwij+UsgnS+yGWWkGpc7ApU3DGDzEgz1P1bw4O4MaWZ2p5XEbf230fbJKk7z/D9e1I7dTWfrufk2AKRCvNnnJVH4E2x6r5TI3gcYazIYexchSvDsrbqoxKw/Rp0Lud84vSlIVBm9/ghI4Nr4mOfLUZ9T+V1HzuQYsnJrTjAI5q9Jvy9OocnYsT334cw7smE2P1hXIEGPbeajYaiJLz/nHfcCJGWzqx6wZVJ+SZQeyOxxM5uiQUr04g3vdLGT7fgx7IuRFMkqD/e6DGF3+d78iDNHHVouNAN/d9CzkTLbfN/FQbUNjcQiJCD+Wt86GU9FhbH+tQxl7oZe9YXd8aew/V9ovsk78UZbs0g+MOPaSoMj//h5xQtksL9Szhp9hTA0nUuVsw9Jjx5nb43ukHK3eOae8beBPpHzPnHL+xqu5Qfxzyh8aypHD3FGvyDsj7CuUlWF3mFk9Fc6uQ9mhiVy1Hr1tOZRf49UuMy6ug9kaaiDCvg2lGTR6MzdX/IKrSxVGHA3izf11tMyR4T11+Rc8rrxFRTgbuxQlc4kerLyuQEv8F0dKK9iuLqVGoaSUHraHmyqHdtkMNt1l3BvkJ3f1MWx6ZA/lWc3In7MqfTY/k9d3mAHdNea0KoJrxX6suh+33TI82cOQ6rnI1WguZ4HHf46kaq781J3uE9VpMAAPj0ynfl7HUN5O31IpKVRnFVeJXqegtIEh5CHevUuRsUhnVj0XofxZQlkDmBR3WfZ7TpI4VaT3X/KY5POoUJbDIHKpv80UwJkj2/DwWMjs0aPo36YqeVJLpCoxlL1hoAh5wsLm6XDJO5rdKodQ/tGJrPUWcO69oZwD53R1mXPbgNLeuMi9NgPHxlQlW5IfcTvxhhBCWPN7QTJlbsPim2FyT8oIFgJZ3bIY6VzbscDHMZTL03/7YwJjh/KhEOAhHg0Kkz5HV5bckL9p5BWnEXi+fyTV0rvw07CDPAWuLWtPMdcy/L7irrxK1SC/7+q6vpRzTSCUjWC1BnN41i/kTP09Hde/xkT07WY6IOzJPgYVy0SOipM5pPvwUFZaQKMK5JB7VbIkKU7jeXcIQx5SVDp8vhnQvjlE53wSUqY2rHmuJeDZan5J70T+dju5bz9JI/RyY6ZDzYGhFXBJWpPx50MIsQbg2TrmfcpyKAeytktRMhezh7JiL12LO+NSpTtj5q9k+bLFzJm/mNnzF+OxeBXL1m7B2/sAJx+GEYGRw+NrkiNXXUbYF93JjYWJv2c2JV/2+EM58tgAKINus2OLNx7TPZg+fABta+UipZSGsj22cscU65GtNiDIh7lti5PmhwHsDIy+BZrAyYMAACAASURBVEkJ6P0vM++3MriWHc9fmujbt9RWCPV/xPbpv/Nbm3fo2I/+mx7EGL5W2yA08C4zGqTAqVgftgfEXH2tfrmbjoWdSdV0GdffWtDFmgdXmiBCe5cNo8bSf8ph7qhBb5NHAZQW+TgfGV0GF5fmzLoQHH0x+x7xhbLBDDoM+G4bTQWnzOSp3ZRGVfKSIn+7d8wp63hw/jBe8+XjHL+1bDrylDcJzCl/aChrLSb87u3HvedYJm57TJg9NCIfw6oKvcnkn9PhUmUmp7Xv6imnxylrW7xeEHUhqLKCzhrKzl4lcUrVmJm3Qgm4v4KGWTORr+koZixZwZLF0d9p7hJvVqzbgrfncS4GaImwhbC+S1EyF+3K8mvhCd7Hr7SA1W8r7UtnIEPD+Rz3t1/IRfaUI/5mcA0n0tRw6CnPakb+7D/Qa+MT+Vwwgs3yDO/ulciUuRHjjkfEabf8z8+nhUtGSrRex23gwa4RVEntTI1hJ3hlb3uswIMDE6icLgdVxzr2lJOSu8YcztjkRauRPWXF678ZWzELWYuN4E+1GL7+v0M58j7lbZGTHI4v/+fsntiGommSk6PJNM4o5MY7KpT7HOANep6cXcfQQdOYcTrmXaG6K570Ly6RLksj5j6FsOAnzG+aFKc8vdhin3MM/eBQNvLnIFcypUpHyb4buPA2+nP8ji2mScFkSHmHsuOJDiM6dvQvgIuUnQbL7kTdk/tgzzBqZ06H5NKdpVflUPZZUJMCyXPTZZOf/V7fyDnlhow76I8ZHcfH1yFXEhcq9t7EZYe5Gf3zM0xsnJfUaSrTe50vOuDS0v8jlO1Bc3//NOpkSoZL9VFsu2eNnm/V+bFtVENyJnOidN8jBCJv80NCOcIAOiwE3dtHx3wSkkstuq44xmV/xyNl4MHZnUxtWY50UhIqjL2Cn9mGUnWP6VVTksK5IkM2PHEYFrXy5KAH9fKmQCo6hv0vjeh5zfJW7wnlG0pMhge413bFtXgHZv1liLU4KpBTa+YweskRfN5YsWDk4MeGsgmM+lcc9ppGz7Hr2fHUcftWHnp3onISiTwNZnFQDUbHoXwLoHnI2n4lkbL8zNC/5WG/yAsjxb1t9K6ShwLdDxJI9BC0ygJhwS84vHocw0e9w/iZTD/oG2P4WmkBjfItx8eXQkqSk2qT9nMv8pYmtS8bx1UlWxKJ0lOu81ItP+s5sjdostofHKJ9zMzK8nxpx0XXeRn1dRXc2jyKFgVTkb7+bA48N8r31dsiSyPhe5cTCmWFCTC+YtPoamRNJ5E6aTKSl+zCtHfcp/yu51A7vhK6T/lDQ1mPmeeXvGiWSkIq1I7F55TR9xkH32bn6EYUd8lImVFneW2Q66fjth3nlFOlyUqVsXu5E9X1NnFv51SqZU+CVHIKJ4ONqN4eo3/JtOSqNoGNV2I9ktX8kD2LZzJynQ9Pw8FsC5LPg/eEcoQJjManLGlVjNRpitFs/iHu2zdsM4ZxZFpPirgkI2WDydFzyrFD2QBG9Fxc0pkSKdNT8LcFMdaoWINvsrBDaTIlK0zTmTdQA0+OzKBJegmXGn+wy9626v1PMbNpEb6TCvHz+MhQ3suwihKpXarQbcNtopphyxv2TWlJvhRpydt+Ny8dzg8Ryh8Zygu6lSBZ1mLU7+bG0OFuDBzixsBhbgweNJye9aqTV5KQMjRk9LEgtMjDYdGh/Bf+GHhyZDbV0kpIhX6hU3c3Bg1zY4ibG/1+qU6xVBJpG8zjshLUYS9Y19WFJKnyUb3zBCYdfk7Q6zOM/+lDQtnAn4NzkzlVUXKWdqVaq570G+bG4KHdaVQoA5KUj3orfHihlec5nm9sTc60ElKhhrTr48awQV2pVKIkhbOlJm3xnizxCUUPPPT+nRIZk5C3eju6Lj3KnbAITkyoEbX6Wgso7yylfbGUSFJufmjZnwFubgx1G0jHWoX5TpJwqj+Nna8tgI0L/2coq6ygC7rEgnb5kKTk5K3aiV5ubgx1G07v33+mUFIJKXszxl5SYrPKx+FDQ1lpBo1Bi8/GLlRMKSElzUflxp0ZOGQ4g4e70X9gB2qUzCrfMtR4HPufy8dAZzBxY0NzckkSUtaa/Np3OIPc3Bg0qAP186dEktJTed4VXurAZPaL80Sv2KG8/JoCPTae7nPjh1RJSOZch0Y9hjPAzY3Bw93o1roS2SSJTI3nc+CNfaHX/xHKFksg+8fVIL2UHOdqPejf341BI9wYNnQQnarmJUPSHFSf8DevzbFuEzKCATWP9s/g51ypyNh4Oscjr0Sst1k59TeK1R7IyrtKLI63tdlXX8d+UElCrxjHxwg6s4mg67v4tYSElCo/tToMY/AwN/q2q0P2FBJS6Q5svWNCaQWNwcyTk6sZO3YxO+6pCDXLc9BPzo+kVioJKcMPNOkxjEFubgzu1pKfskhI0vd02/yAAD0YrOB/fSfuY2fideY1Aab4b5VKKJQjDHKPTfd4Db0qZJaHaEt3Z8ZnfKLXh4ay0iyPCv3lUQdXSSJziV/pMtyNocOG0qt5JfJLElLRbnje08lTOAks9FrTNQMpk2YgVc5sVG/Vn4Fubgwa2JFauVIhSaVpse0+wSYwWtX4eP5OPikJ6Qr+Qst+w+X3DnWjfePCpJEk8vTfw/UIG1aCPyyUDaDGxosT7jRwkpCyFOGnrgMZ5OZG3w7NyJtUsq++nv7OUFZZQe27kyHVMiFJWSjVoBf93NwY5jaUbk3L4ixJpC4/kBWP9PLdbY/2MbJ2BqSU2SnXcihuw4bwW5NaFHJyIUf2Ivw8Nnqhl1u1ZCSRMuJcshTNug1lqJsbfTo1okQKCcmpLZNuhWH6hEPX31YoB19ibsdCCc+HZMhO5TZDmb3rMs8s8smrB56enU8DJ4kc7bfzHNDqwji7sSf1Cse67zStM9/3mskKn1CMNjCY1Fz/qz8F0tj/e2Nvrj8/jluZJCSvPINTYQ6hHOLDuIrJkErZ71M26+zPvm7GsJWDqFPhu+jPyVqOzuP2c0tjQW21PyUs8AZzezSQLyokCSl5LprN8mL8L64kd23OjLMh8sIhvx10r2ffVopWzL/my9FxFUiVtipD98hBYLGG4rN1Hj3qxS6rsvz823Q2nfSzD33bODO3Ka4pc9Nkwc2oUDYBPp4dyZ/SmRpTfeIN5QgjGNHhd3k3f7T/iWzJHD/HlfzVhzJn1SWe2J/ZawCenV9MI2eJ7L9ve/czyPXyEKvaqODsmgF0a5Y3zrFOkbYorUZMZPM9M3qrHKgqsw1V2EM2je1OjcIpYv5OjhZ0H7iJc29M9u/5lHn10yPl7o73XaVDKPuz8lcXpGytme8TigawmgM4sXw8v1V2jrnNpNmp0n42W2+8JRQwo2XvsLKk/q4S/Xf4RoWyERNHJ1YnY6ridF77KM7wtcoCEW/Os2ZSM0qmjFWnC1WmxbQ/OelnxBLfE9bMYNS95eKWwTSslJ+iP3ake7fe/N6+L78MmsW8M8HRj5r8VMxyWd8/NJw2VVM57G8y8tXoxrAj/ijN8kMr1Ho9ZyaWRpJc6bjzNYEWea5fh4Izs4bTrEzMZ2dLLs1oN+8sjzRWdDa5sX7i3ZwMUkZqz7nCM6v8IJW4wQABfw2hVKq0VBhxgLtmh1EFI1gI59yiLhRKKSE5t+SPo//Cs68NyKEc/oD1vfMhZfqeqT7yFIPSXs+1gCLkNut6NKFYZsdjnwHnykOYcsiPcOQ2Iva2I4evV3V0wtmpJQPm9qJivuhtJMn3E/1mnOCh2f6HL0xg0Dxj55Te1C6WKma5ZyxBvV5eHPXVoQB0Nvt5kPU3FviEvTOU5Sf2hXB+/UyaFE/msN2k5KzbkHKFspGr/h9Rw9fHJtUgU6qitF/9MOpcUBjBhJI7h7wY0qJMrPvEi1C+8R947r7Pa+RhfLMxBJ+t02mUPXnU+wq1cmOse08qO6ehxMCjBCMPXw8on4pcZfsyfOavuCaN3m7O6l2Zvf0egXz6P/bxbYSyGfTKAM7/tZZJ7jPimj0Xjz0n8AmwYAD09rkZtRkCX9xgy9IFLP7rCYFm0NhAY9Nw69Q+lkX+/vRZTNt1iDOhcu9AaQC1BUK1T9i9dgaTpnowe8sNHr99ydF1C5m98QpP1Tb5Fg8TKFSBnNiwiJlrz/NACSqjmZsHlzDbYx+XVQauXzrAzBnyZ63cdwV/7H9m0n5bhdIG6me32b1wHpPcZ7Bo53leWg3cO76eWYv3cuaFFpUZ9Ki5fHo9s91nMMljH2dehnH3zBY8Fm7jyCMVSjNEmMCGBf/7Z1i5ZL69jJYy2/sG11/YHw9pf9jGs8v7WDR/NduuhMh/hMMgL3jwvXGE5fNXsuF8IIoEriLlEwnUAXf5c+MK++fMZeqC4xy6acZok/8oSNRxeHmTrUsXsHj/YwLfd2Wqj/xTfAYCff/Ga+0GJrnPZJL7DCa7z2Hd9rP46eUhOMcehMoCVv1brv69g2kzZtr3aRNeB/wJVMujEvKDAhRc2LGMGZ7HuBZklIdVjaAyabiy15OZyw9y4bVe/rkZbFY9z64dYcF8j6g6t3DLcS4H2e+VNoLabObe35vxWLiVgw+U8mI0A2jMVh6d28H8BevZezs86udR5WhvnCNUr/l7yxr52LrPYNLc+Sw8dQv7LeXxP1nNvmjLiIkHp9bj7jacfoNHM2TCn+y+ocPwjsb0n1CaQYuRJ9e24m2v15NnbuPAzZAYIaLUW3h+cQdzPNZz4LGG0Mg/rGAEszGCO+f2Mnv2bHuZrmXZbj9e2h+TGtmbDHpwgj9a1WXw+gs8MMh1Kr79CX1yGq8Fy/A+6ctrY6wetQV0QXfY5TWDSYv2c/K5/v1/NvRTMIFeE8b1I2uYsmQTp1/HHIZWGOQnUenCfTm6e429HGYzedY+dvgoUZHwQ1SiFnr9npmsOQazXanh0tlduNu3seboHUKQRxui2hkz2IwKbp3ZzcxZs6Lq8vK9PtyNkOf31UZQRp4Hyw5EnwcJfEeFPvouiOcXtzF35gwmTp3BxGW7OPP8PJObFydHleGsvy+H8qNzO5m/YB27b4XFOBcU9rsMQp9fZp3XIvu+LWT6iguceyyvS9HZH9UZYQW9Ss3t3RuZ7T6DKYvWc/qVkrfBN1i/YDGex18QAQTd3kGfkikp1ngtPoRxeJsXk9xnMMVjBftuBaIDtJ/huH8boWx/hrLhPUNtRlvMW0cU9oU+kfOdkYsHlAlsy3HxWOQzsh0XqmpM0fcxxh5KNMb6eeT8kMEc97O09mdsxzjJiPlZWodn2Rrs9wdG3iKAw88j58BMtuhtKozxP8PXQMzvF/meqGfFGmKuCrURdz9jnJCG6OfQOr4sxLz6VNhD1vE4fOixT+hZxPE+19cAEfGUNxC1SjiSwXE7Dvvj+Dd3o34e67nCkS+jw4pox/nIGMfCoZzNCZSnQk+cvzUd9T1NCQSyo3ieO2wh/tuoPqXYZWIh7qhKZF0yx149Hs8zmCHuuaFDy/b2fZi9/SKPTPE8L93ePjiuKI8z96yHCIf6F/UI1c/ZZkUeW2PM8zi+v/OstMY99pFz6AltN3r1dSacs/VlQ5AlzpREvIGewPkR+/Gm8Z4H8dQ7rSWA4ysWMOIPL476x9xmwNnpNMydhqJtl3I4RP5zmpHnguNzqx3LSk3cl4lYvVl93O9hsskjbJFz5Tog8PYO+pZKQf6ay7kca5sWPq4d+hjfRigLgvDNUZpA92wlQ36Zx6ZTIXEf2/kNixPKgdG3RP1r+2EEA0Fs6iA/9CVfve4MHjuBMRMmMH5oH2oWTIaUohR9Nsiryz/0CW2fgtYhlAvUWs6lhC5SPgMRyoIgfJ1MoHp0gkOX/Xge9o7RkW9Q5LOvlzSVSJq8A6sC9P9+KBvkER7/O+v4o9uPuMZe51OmDh3mn+BGsBXzB/zVuU9JCwTc2EinnBKZy8/jvAhlQRCET8B+/7LmQ4bxvyFKMyhUSs5vnc6k6fvxUZij1oX8q/tivwUv2P8OOxd6MH7sBEaOncDI2XNZcukZQfLfa/nXj53aAm/f3GXHwknM9LrM00/8fOt3EaEsCILwLXJYS6Az8s71H5+V/Y4JC3FfGuN/dDFlf8Ru5Fy9+l/cBxHKgiAIgpBIiFAWBEEQhERChLIgCIIgJBIilAVBEAQhkRChLAiCIAiJhAhlQRAEQUgkRCgLgiAIQiIhQlkQBEEQEgkRyoIgCIKQSIhQFgRBEIREQoSyIAiCICQSIpQFQRAEIZEQoSwIgiAIiYQIZUEQBEFIJEQoC4IgCEIiIUJZEARBEBIJEcqCIAiCkEiIUBYEQRCEREKEsiAIgiAkEiKUBUEQBCGREKEsCIIgCImECGVBEARBSCREKAuCIAhCIiFCWRAEQRASCRHKgiAIgpBIiFAWBEEQhERChPL/QWEAtRmsCIIgCF8KA3L7/V9niAjlT0xthIBwLSfOXmLf4ROCIAhCIrb38AmOnDzHvacvURv/+wwRofyJWYFjpy+SNFkySpYqTclSpSkhCIIgJEqlS5cmSxYnOnXtAfz3GSJC+TOE8t5DxyleshQaC2gsoDIJgiAIiRHAqHETafFrKxHKn4vKCkbAkgADoPyMobzv8AlKlCotQlkQBCGRAxg9fhItW7URofzJmeQwVoW+4eqFixw7HY8zt7j50ogO0Jk+/cS+YyhHHvT/vFwEQRCEeEX2lEUof2pGUKlVvHh6izVTulCnWDGKFy9GkaIOihejeMEqNO6xhN2PXxOo4pNP7ItQFgRB+HKIUP5MNEDQ6fm0qZAel0aT2XbPgslsIVzrwGzBHHSFRT3KkKFUCyYe12L9xAdBhLIgCMKXQ4TyZ2LCwNEpjajToAuLb8oFrTbHpQO0dzfRq2k9aow7RRCg/oT7IUJZEAThyyFC+TOxEMzGXj9Qv/4YdgfJBR3vfDFAyEkmtmhCmY7beAhoP+F+iFAWBEH4cohQ/kwMWDm/oBU1azRi3NEITIAZeeGXI7Dgf2I2zWrXouGsq4SLnrIgCMI3S4TyZ6K2geLmZgY2yEOxZiPxPHmPO/fu4XPTwb173D+7gzG/lydf7b4su2LBavu0+yFCWRAE4cshQvkzUhps6A2v2ePek+oFClGwUCEKFHRQqBAFS/xKP89rBBjMKA2f/n5lEcqCIAhfDhHKn5MR1BZQao0Ehql5E6rGP5Y3YXre6u3D2TYRyoIgCN8yEcr/AqUJNFbQxscGaq2a589ecf+VmlDjhwezwiCHrIGEARz++6wIZUEQhC+ACOX/mNoGqsf7GNuiGU2HH+SxDTQf+LsqIwQqdNx+5Jeg535+eK3bRMnSZUQoC4IgJHIilBMDnZHAt0pehxkJ/4jfs9rgwLFT5C9QMEEFCxbENXsOypargNYqQlkQBCExE6H8uRlBYwM9Mp1V7uHGeI9J/m8mPm5OWWmAt2oTvgGhCQoJDWXT9t2ULCV6yoIgCImdCOXPyQQao5nA4GAe+frz0Nef5yF6FBbQmh3epzUQEBzBq9CP6ylHGOSQ1dkSBnDw+BkxpywIgvAFEKH8GWksRvzPrqBH/YLkyl+QAgXyUbXDH2y4rURlAZ0Z1IDqwQ6G1KpBjT57xBO9BEEQvmEilD8TtQ3CrqykU6WClO+yjMMvAwgIuMfWsa0oUqgZA7Y/RW0ftlbdXU/370tRut1W7olQFgRB+GaJUP5MjJg5PecXatVpy+yLFrDfoqTXKji6sCc/FK9K+0WXCDKD4eFmelcsR/lO27kvQlkQBOGbJUL5M5H/IEUVGtQfza5AwCbfV6y2gt6k5sbWkTSqXoH2no8JebGbAZXLi1AWBEH4xolQ/kyMmPh7RjNq1+vEgmtyQasM9gd+WMBo1XFnuxsN2rSk15TpDPupEhU6i1AWBEH4lolQ/kzUwNuL82ldpioNRh3gvtaAUh99y5PSDDazP7tndKJsxgykTZqLn3ruEnPKgiAI3zARyp+LETRmLb7HlvB7lRyU7b6QowFgska/R2UGrUbB8aU9qJAyKz/328cDEcqCIAjfLBHKn5MZtEYDL/yeccs3lABt3AeHqK2g0qp5/uw1TwN1hH3Es68/hAhlQRCEL4cI5c/NBFrAYIvnSV52SrP9iV9W8VeiBEEQvmUilL9yIpQFQRC+HCKUv3IilAVBEL4cIpS/ciKUBUEQvhwilL9yIpQFQRC+HCKUv3IilAVBEL4cIpS/ciKUBUEQvhwilL9yIpQFQRC+HCKUv3IilAVBEL4cIpS/ciKUBUEQvhwilL9yIpQFQRC+HCKUv3IilAVBEL4cIpS/ciKUBUEQvhwilL9yIpQFQRC+HCKUv3IilAVBEL4cIpS/ciKUBUEQvhwilL9yIpQFQRC+HCKUv3IilAVBEL4cIpS/ciKUBUEQvhwilL9yIpQFQRC+HCKUv3IilAVBEL4cIpS/cv9lKCuN7xD7/fafx/n9RFCGwpctwTr3rvfGI8Z7P6ROCx8ndhn/27+fSIhQ/sr9V6GsAWy8+6W174vSChb7z6yAxgZmh3+rjaD4h/ujMIPOKn/mx25LYQC1DUyA7v/4/f+MBQxWUNn/bUD+Dqr/Yl8cy9Mo74sVUBs+X3kqjHI9xKEuaROo/woDqGzvrrMGi/39RjDG899tgOoT1FW9w3FSGEDn8O/I/x/7nFDYf88CaD7BPnx0OVtBb/6I3zOCyhpdpkqzXKZRZfwBlPbfV9q3ZwCMtk97caT/gLL/R2VnkOuoCdDatytC+Sv3b4ey3GOwcmldfzr+2pjatWpT/edacXVcysFnGjRWeHt1LYM6N6RWzVq0nnuOy6e3MqFPU2rVqkVL9/34BIDJ+v/vk8ICtoBLHL94iYN+H3fiRxhAb9Vx/a9FTO4/nT0vPrLx+Y+oDKB79CdrfUJ5rgKt6R5rBvRg1rYn+GpB+199BwuYQm7g+ccIes0+zG29fKHzqT9HYQbMGu7vnUztmrX4qWZdfu2zkrNah4Y8kgn0OhUP97lTs2bMelqjVkMaNP+F5r+1xeOMnnArhEdcYUn71jSoU5sake+tWY8600/wLNyK/iPrVySlETTWJ6zr25axSy5wTw1m6yt2juvH1OUXeWRWcmDKAKYs+pubEQ710ARGzSPWTejFsFn7uBL+8XX8n5SzKcyXu1cOsvm+XK/eG4om0Kve8vLGbtbcBI3VyosHO5nSshOLL5kJj29kwpERNDoTins78b6iJdAA4eHnmN+mFdP3BfBGB5p/WKeURtBaX7BtdB/cV17msTmcfRP7MWXJaW4rP10bYLRG8PdSN9z6zedQABgtIpQ/X2U1gNZ+da4zR19ZKUzyFZ3jy/wZey//eiibQKU38edAZ1K7VqHL5MUs8fJk8TJPFjrafJHbIUb0PGddm9LkK9+KwbO92Hb+NItbVaBoueb0m+HFplMPeBL+z0LEQATnZrelU4eJeD+Ty+Rjft+CkqMeLfnZ9Wdm3wbzP7hA+DcozRChucmqluXosPg+D7VgMJ9gSI7vaDr+PHfVcg/6P9k/gNf76VO5EC6N5nFaD6ZPfYFgAoshlBsbxlGjcn3azPJk1eJpDG1Rh/IdV3A6HNQmh/AwgkZv4NWdv1nkUEeXrvJi7qwulJIkpLJ92fPQjB4r/reW0ihVAeoNmofHCk8WL/dk4bJVLDn2FH+17f8OBKUR1OZQLuzcwt6zL3ilAzO3cS+XhdqdN3PZGsTi6i7UbLOC46FgiTyGZkB1BrdK6SjWYDoHgj6+jv+/9Bh5eMCdwQ1bMtknngueeGhtFl5eX8uEujUYcgTUFhvBAXc4uHYbJ32tKN4zFaC2wdugk8xvWI5uG4J4bQCVxo8T6zdx+I6KEL08YvGPziEj6LnBxFKZqNttO9esb5j3QxZqtVvNqTAw2z5N+WnNeu6fO8Cfu85xO1weyRGh/JkYrOGcXPEHQ4YuZ/9ze08PQPuUrbN6UbN2XWrWrsvPtTsxZtVdggDTZxga/a9Cee/IgmSrNpr9QQkPB+psAPdwL5ODpqPP8xqA18z9IRcNBxzmqf19GiNEWKKHuCNfcYaiLdHD3lGfYQFQsr1PVRpUH81fKvnnkRdNji9bfNs0gBkVfy/uRJOiTVl4Tz6WkcNtjq/Iq2eFXh6aMxL9HhvyUG2ESR4Sc/zMyPdE9RhjDbuC/N2V9m0r7cOnBsfv6XBcVYCBMwzP5UTnJb6EABbrGcYUy07b6Tfwc/i9yAvCyO+sRq4zjp8bOVyn0Mv7brbvc+RQ73unGMzy8FzUK/wkbnXKU7D1Ms7pwfiJQ1ljg5D7exjfuDiVRp7C3/6x/qdX0KRKQZpseI7OFLPhVtjLLfbr2Z7BlMnUlOHn3srDjGY9vn+OokjWbqx4FXe4O77ha539v6lNchlGWOQys0SWvV4ud5v9ODoeUz33mFOjAM377uSaNYSVjQrTtNsaORgcQtmmusD42jmp+NsiTsWqPI6dgsj3x1d3Ezp+8Z0rIJ8rNmxc3dKXJtlrsvS5vQzs9dwQ6/0G+2eYAN8rs2mVqRiTfOTv7XhORB0PS8y6CGC0yuUZodhFzyxZ6bddjcr+86hjYC/TyHMh9jFSO5zjsafZLNjrhRH03GbGj/loMWAPN6wBLK1bgGY9NnDeXhh6i/1z9NHtU9TPYpWfPtY+RLY14froc8Nslv8NIpQ/CytvWNuhFPlyt2budXslfnWGJZMG8EvfScxesoS5C5awZMlSxvXpwegxKzkb9umHnf7TUK4yhC2PNGiIOxKgNIE2+Bxz+zWlcIbUZC9YiR/rNqBBgx8pnCkNrvnKU6XuSDzPvyEUMD7Zx8R+LahXrwG16zeh/bzj3I6QyzWyUTO+PMlct9/k99RrRMOhazn25DHnVw6lYq7MOGfJx/cNRzDvSBCg58JGd35t0IDa9m12dd/CZRVYbTEbqBihfNcesAF32TCmMbXrNaBOgya0GTaHw4FgsoDWqufh1WN4VFKD8AAAIABJREFUD5qD97qp1K3Xgnb9tnMD0OkUHJr3Cw3qNaB2vea06+rOhs2edB24mP0vrfZ5MhsP9/5B88YNqFOvAXV/6czkExFobaC32gh+e521A//H3nmGRXVtDXiSfPfe3NwkGo0x9oYV7L33XrCLxlhS7L0i9h5LVOwtiUaNscaugNgVLCCgoBQLIk2EKcwwfd7vx5mhiUYNTdzzPO8TDcXT37PW2nvtFezfs5pujt1p3WEFxyOTkJultJ08/Awz+zWn9Cf/oUjFRjQbuYNLT92YVas0Q2asY+KQ7+jSrj1tOvdk0Zkoog3SQzkJeHx6HsN6d6BV2/a06dSDWQeDCddL4tSYoziycCquew+zZeFCvm3bntYduzN5x3VCtNK+p3mwa6WHnin6Guum96ZN2/a06jiYZYtm0799ayr3Xf+ClF8mgNQPs1fWTPVgNKu4tnM4TeyqMuMymCzS9aGODWTTkPIUbrmBW0ZIfEWqNcliIubOXoZUK0KlwccJMUr7okmU47V+AIV7/05AKrOZyWCgogEUSdEc+Wkk3646w91nFgxmUAceYcKIQcw98pAnWtCa4c6R2fw4ZR3HwwI4uHASy3bc4r4azG8gZZfWZWjTZzxTXObRr317WrXtTJ+FB7kWl3KfqABj9A22zu5Hu7btadW2E92df8EtMuV7Uu+D3ACQxO1jq3BKvlc60X/GFq7LIfjkEjo6lOCr/xagYtMhjN10g2dAUvxDDszpLN0f7TrRa+RcjjwF0BB6aRNO9crz9X8+pUxdJwb/7I5PyGlcR07mNx8jCVahJzw4yWTHjrRu255WbTvQoe+P7AyE+LBjTOhUm+L/+ZjiVdvSceoBvCO82D5+DOvcnxGpBbVRuo5irqxlaN+OtGnbntZtu9B51WUeqaRIVwOEHJnKoB7SfrXp3JMFJyOIMkjP4aQXpFyOHqMOsXfvz0xau41TIQZMZqm2rQg4xPR1O/nDR5mm1CYdvzhOb3DGsZ31+LXvwvDVp/DXAGYll3YsZuHM7XjGSP8uwIz5S+nRq4+QcmZiIoo9wxpTzWEo6/wA1FxZ6USruk4s9E77nhp+dAZd67Xi211hGQrsn5DjkXL0yx+wScpQTu6ZS6cS+aneagRTlrni6jqdLmUKULXpUCb8dJCLYSqeBx/CuUN1qnf+kamurqxb9gONq9WkvfMRbseD0QSJj9xY1KceVVsNYPwqV9atnUPPejVp2s2ZjYd3MralHZXtWjL4p4N4BN3Dc8twerToxDczXVmz1pV184fTrk5xqny3mnPRaV+OUkt5XShon1xh9axOtPx2CutcXVk+exSda5ai7rA9eKnATBK+x2bTXPYVNftPZ4brRrbs9SFEEcGhWR2oXbo+Q9a44rpyKS6961GwcEFkn3ZgZZAFjV5LwD5n2jg40GrSala4rmb2yBaUr9qd2SejUVsgLmof3/67MJWafM/wpWtZvs6NW/F65EYpYlU8v8u+DRNo+eXn1Haczuzfr3Ff7snM6iUoW7wa7Yb/xAJXV2b1qUiZWoNZ7Cm9+IS5zcJxYDeGO69krasrcwbWxK5qT6YfCSXaAmaCWNmiNEWKlKLV4EUscnVl2bAmVK7SmuE7/HliSTuQKtEMMQG7mdmhHk26jsDF1ZV1s0bRp0Ul8n9VnHpDtqaVsgGM+kjO/jKHHu070uYFujLE5Q+uqF4+YEthAJM5Bk/XodQv25ctYdIDWmUGgzKMI9N68HX5eZwyvVzKKhMkqp5xZIY9BT9uywL/RFQm0JhAIZdzbGYlanV1ok27rrRv35E2nboz8VCUNJgw1YuJyggqnZyjk2rwcdM5HArVYgSe7v+BL2Qy/tPzF67HSHHioZHFKdVmOgceXmVJrQK0HXYALwXwBlKe17UKXxf+mgZ9ZjB9tSvrVo6hbb2aNB+9i2ux0n2iibqC69CmVG3UnZGrXFm3ejLdmlajzoC1eDwFsyntsTSYnuO9ZzL9Wrak9zRXVq91Zd3iifRuUoqKfeeyw9Od+YOa4FCwPN2m72DvxcfEPr3N1gVdafbNONa4urJi3kR61ilG9UG/cjVBT2zoBVaM60LNz7+mzeht/Ho2kFvXltDjv18x8pgZldlMVMAepnatQ9tvV7B4tStrfl6Gc+9KFK7XmwWHLrN16XCafpGPht8sYdmhWwQ++YPvv/iEvq6hPNJK9++TC2vpV6cKDYbOZoGrKyt//p5axaritPQyDxIh5OR0Og7owdjZq1jr6opL38qUrTmQBe5PiLVIpYP0UnYcfYSzf8ykahVHRuwNk6J0LFxf2YHWg6axxV+LhVTHTx/OyRVOdGrVm6HzrM+a2YNoXL0kdcbt5LZBx+ExdWlcZSDbH1pfjEI9GdS0Kr2/+V5IOTNJI+UAgBDW9q9C3aaTOBwjRWMKHSgsQJQ70x07Um3wAUIBTSZuR45IWWfk2DQ7ChcuTY3m7WnfqRPtO3SiXSqGbLhKkAIglBX1S+E056o1XR3OmmZl6DP5DIEAPOfE6AaUKd+HpZdtiUAtQdu/x778AMbuC0eHmnMuHalcuh1Tjyckp7zCr53g992XCbFoODOlFV3bzuKEHtCdZVyj2jj038WD5FcEPX5n97Bmjzs+cWlr2ClS7s6WKPDfNZ0O5Vsyw4/k7Tm/uA0li7Tm57vS7wo45UKTj0vSakVwcto9xG0xHYt9Qsf5oViDF+L99tClnIzPivZi4yMLMU/dmVmtEOWcfsdPZ/0mpR/bhzSmcOMNnFfriYs7xPeFClD228M8tG2BASmNppOiIQNezKpYjKGbwokDLJZLOFcuQrF6E9ln/SFL0HpaF/qc1vOvEGKOZEPTSjQesxcfWw1AeYyB5fLRcOxhbqgBglnfxYGipTuz6KJCSvslnOCHqvlx6L+Z84pUA/IMYDI+Yc/Y6lQo05mFF23xrxK31b0o/y8ZNQb/mlbKetDq5fhdOMKqFatZ8gKubNrvTaDa+vKR0TVoBIv6Icfm9qVslVH8ES1JWWkCizocj0X9KFhhFHuiJWlmJGU1EBV+inHlPqHakOPc04PaBGqLhdgod8aWkJG/fC9+WLiKZatXs2TpUsaN6sXcE6HEqFO9MOilVGn0X2MpYdeTGR5RPAdurmmIY9OKyJosY1+QGpPmGs6N6uL000WCDcGsalSS7hOOckP5hlLuVI7/lmjHnDNx1mvOhP+2QVQt2ozBW0JQY+LGqv7ULlqPobuiktOmkW7zaefQlk6LfYnDWmaxPZvkXizp3ogyrVbhn+qFOuTqQVb9dpxAIwQenkDvcu1Y/xDAjP+hxXQrVY/xXik5ZZ/13SldsAELb0nZjgi/NQwqVo15N6SvP7ixim++Ls8kN9Dpldze+C1fFujLzlSpeG3EdTZt2capxyBXnWJ8yRKMOahDC6ie/8WYEkUZsvkRTwyg0d3BtU1pSrZcyLEI229Iwmff7+w8G8YT9V2W1SlHsxmnuW/7cuwfdC/2Oa1meuKfBGSUvh62D3/5fWbWrYXj+MP4agHzPVZ0bUTfsTu5liQN1ko+fhGHGOhQk8YTTiWXUSCRayd+Y+3+qzwyqjkytTUdGgzn9ycAsbiv6k/hD2V8O2wskPMuexXvnpR/bET16iP4NVw6EfvHNqZV84kciJJSagodKAHz4+NM6tmRuqOP8xhQZ+J25EykbOTotHIULteEAVMXsWj5chYuXc6CVKw7E8zjRDDgw/zaJeg59TS3VLYboRSOow/glQhEHsCpYmVazDhHOKk/t5jc0IHGw/4kSP6Yle2KUq39Yk7ESl+Va1NqeRDD7uFN6NBiMnsjAU0Art+2oXCJLsw9+pBE0n7S15VTpOzI+lB4eu82508/Ri59N3eOzKZ59eIUq9CMpdcB9Nw+5oxjkVpMO28kCbBoIzm3vD/Fy/7IXrmULlaZQaeJw92lHl+UdGLzPTn+BydQuWAL5t1Ou026m8uo/UUdpl+KJzzqL0aXKEr/TQ94miTVDFOfA5UJElTuTLYrwoCV/oTqQGu8gHPlonSd60WwRrr+krSXmFr1K7pO98BPncCNo97cjbTW8RIus2RCF8rn/z8aTzyMVwJAMGvaVqCV0xbOxwMWMFtCWNOtPK16/syp2FQDYMxA7ClGNGxIk9EHuGsGo0F6YdCH/Mmw2qUo329zhunrJF790bwqfW0CEu6zb2JrPqkynN2RKVI2J4bjvqAb+cr2Y0PwS6RsAJ1RTcjRaVTKV58JZ1UoDNJLgMoIzxXBHNu8m01HgpNfrACC1rahZKOfOPwkiURSsl0qIygf76Jn4dJ8s+4uD0yRbOnYgGlThmFfdRbrrkWhfbSdloW7MfZAKHEEs6z+20l5VqsiVOmynFPWcRwKgAd7+aZqVaqNPEZ4/F0WdKiJveMGvNMc0Sh+GdqMKs1cOB4nlSFsx8KcFMauKd35umBLJu68S/wLZ0PN5R0j6FamJctvg9Fs4OF9f84dC7N+r5Ewt8W0rl2aIiVrMucSGDERdHEJ/YrYM/0saLEQapPyaQtJJh0hHnOoJrOn/ejDBGRwDUQ9+YNhxYrxw45oos2QEPcXY0oWZciWx0QYQPn4V3oW+oxOK+4Srk3JfNlylImGp5zf782959b/8ewsM4e3pexnH9Fqtid+SshQyj/swdti4tKMmtToOYXdocD9jXRu4Mgg1xtEWa9P2/GzKL2Z36kxRSo6sfxc1Au1ZYhl/ySrlCMBzVXWfN8Hmaww348cJp3HXOCzl/FOSdnMU3Z914CvZAWo0LQHfQb0o6VDUUrW+I7lN6VTI7deIcG7xtC0QhOG7IvFSObOs8vR9HUTZ47HvuTJivSwVHPr5VJWAwHraWVfisJVm9OuhyNduznSuZsj3bs0ptR/ZJTru5lrT+6xuH09Ovy4A6/Ub6pWTMSwZ4RVyk/AjJGndz2Y16c+lb52oHl3Rxx7ODF61WFuqqQ0X8ZS7sya+4ApgSv7ZuLUy5HuHbrTvV0v2veuSbkKLVl2E0CP7zFnepWoywIvqXaliwti18iWFHZYhifW1KkRTElx3Fw7iC9KD2CLfyy+Gwfw709LUKNtdzpb97WrYzfaNirDpzIZTruf8eDJEcaWKsawHVFEaF9M5b5KygMW3yBILc3nVHOD+TWK09vZndtJQLQ7i6d8Q3dHRxwbdKfndz2oXaEALSYcxlsOEMzqthXoOjhlBKqRcLb0rEqX3qs4/SxFyiog0Wcd3Sq0oN2cCzwBNFqQW4CYs0xuXx+7PhnXlP+RlI1g1j7h1NJBOFSRIuI0kfLCARSqNIWD8ozT1yozqOMjOTjJns/rOHM0xoLSNqJYK13ftkSC2jogSGM0EXd1BXW+qMzwg094qk+ZkqM0gNIYyw6nEnRf7MHdx+6MbTmVTSc2M6zKIBYdfITXHwMo1Xg0G2+oMHCXxW8l5UvMaFGfjsN+x0trvQdMgPISLu1b0PyH/QQ/vsiUjhX5tHRdWnVPdS85tsK+kIwvaw1iW3CqkdtaUJvMRD+4ws8/tKZKgfI07u5It+59+XHBL1xKgPRS1lnAoEvk1tH59O/tSPeO3XFs1ZMOTvUoW6ouc6/8jZRPGVGYQal9wLGl42j6vwI4dOhK5+496PP9JHbdk+rNsRF7Xy5lPSgCNtC5eB3G7ZMkrda/eI9oHh5n1jgnHB0d6Vq3Oz2+d6R66Xy0n+2JX/KzKL2Ud3FFDerL06hZqydjj8h5cGoobfoPY8VljTS2QJfq+Jn1PL55nKkdq1KhZC1adnfEsecAJm45S4AG4FmylHfHgOXBn0zv0h2ZzJ4fRnwnpJyZaIwaAi+788u6daxaPo3u9gWQyWTIZAVpOvko9y1gSAhk308DqFG7OQNnneZOfObPgc2tA71s36v5Wymvo0Xl8pRrP4zJS5awYNES5ixYwuyFK/jJdTO7rzzgefx1ZrepSqshW7mUmOpN30oaKUdIUaIekEfc5sD6lSxauoRJ3zSkfOGidFl6kWBDWtElS7lyDzaHGrl3YDKd7UvSbPgSFv60kb0nrnF6x2Dql2rKklRS7lm8DnOvSlI2xAezb3xHvnpBys/wXj0gWco+Gwbw8RfV6Dx+IXMWLWHuwiXMWbCUeUvXsGrL73iG63kacYBRJYrx/a9P31jK/RZ6E5holbLFmzk1itPb5Rx34kL4ZWB57Bs7MXzWEhYtP8rtkJNMbFaMViP/5GoqKXf+divnntuk/IhNPRzolE7KSkAbsJWeVdrSbvZ5ngBJOqzlGjcmta2LXfrR1wYw6KM4t3MR/R170OUF+jB83n6u/W1NORbPtd9Sp1QnXAOlWrDKDLqEEPaN6yDVlM0ZS1mDhciwY4yxL0Cdaed4bB00lHzNWkcEG21NKvSgNpqJ8d9M92KVGb7rMU/0aVPYKh1cX9MK+29W8PuWidQftpXTYeEcGtGIb1ceYOmQynSctYsr0QC3WfRWUr7MjJbVaT10O5dt94AJeO7J5BYNqf/9PoIfX2RSW3sK1urD6EVLWGi7lxYsY/GqTWw/eZ07CWmPrVwrSVD1PJhjW35m0dIlTPuhAw5ffUbz6We4jxHv30dKUvYDM2ruHptLT4eiNPxuCfOXrmPHwaucOzSahsVqMefy30nZQLwBNBbQo+HG4fUsXbqE2S5TaV9aRplOKzgaqScqcj8jir9ayp2KV2H4roeE69Puk9IAiXE3+bl7KSq3GMroOUtYuOIkASGHGV7nazpMO4PPK6R8SQFoLjKpVh9GrD/K2on9+XHsejwSUk1TS3X8DEBc2FX2rFnBoqVLGNfLntJFytN/nS9RqDkyRZLyrhiwhB9iZo++yGRVhJQzm7TpU4jwOc9vazfy8+qN7DjhT7ABtMpwzuxZw9xt7vikGkmcmdvxLkv5mgpIOMNwh/J0n3stVU0m3ccYwNJ2JXFoOY8jqUaRppSjnrFreGM6tZrKvqfS1IUXOjIlBbK+X0Vq1fiRnRFp5yInS9mhHzt9n7JrZBtK1ZvHxeQfluOxqBVflWnOCt8Xpay2gNkQw6W131C2ZC+2RkgvX0ozGLRyLi1syxclndgUouLumZk0LtSQFfdftrMQE/Enw4u/npS/XX2HMP3fSHnmBe5d/40WRRsyZGeoNS0PPPuLPvb/o86Yg9xQvJmUpTSyJ+ObVKXe4F/xMYDFKMma8KOMblQeu76bMqgpx3PLfS8L5yzA5QUW8/Pvlwh4VU1ZDwaLFp+D0+jgUJkJZy2YLNK1oIryY/WgEhTtvYcQgyTl9D+vtegJvjiPtvmKMWhPPPG6lIg40WAiKvAvFoybzu83tcTppPS0xmjg2bmlVC3QlyU3nvHMkrYElWiAqItLaVx5Aj8MKIH97D1cewJhOztTY6AjTQpXYeKWm9wzgBnft5TyVWa3LoJd+8UceyqdPiXAvR30btqU5nOvokgMY0W3WjTtvQmfl1xbBus0HYUOFNqU7mtpPqYn7Bpbj5ql+/DLc/DZPRLHsq34ORDQhfDnpE4Us5+KW/JcoyS813WjcPHazL+aImWnog64nMtYyumnBALcOTacFv8rwZgT8TyIOMyoEsUYvis2rZQ3PyLCAKqo3fT/6nPaLPLlkSYle2ZLHyd5L6F6gSaMPRqV8m9F7KKj3Sc0dfHEPxFeKuUEgOfs/q49gx2bUKH6CKasv0EkqVLXtuOXKrOS/FF4sahNMeo2m4ubAU5Pt6avnwL6m2wY0R2ZLB/fjRwhfXsu8NnLeKeknBq59sVpHmqddMGnloMFUOUlKf9N+toEJHGTeS+TshzMJHJ1QQdqV+zC9FMxyTdVgv8OZg4dyKzd/jxBTcC6gVQrWptBv4Ql14ifXtjCBJef2RcWzNGxTalfZxy7nwGGQLbNn82Ejd6pRP+MXaNbUKHaCPZGp+0glixl+37s8Ith77jWlKk5mdPWkycP2MmgEh8hK9mCOVfhBSmbQIeBB9e2MqCcjNpDLyTPFY69tJKGpT/h0yI9WBdm4XnsDda2K06lruvwVKYcqxu7RvHdkAUceKTlaeTB14iUTzK2RGE6zPclGutAr0ovl3KQ7x46lKhK/02B1lqpmgvTOlLwQxlFRx7kRjxAMKteV8p60FniubrOibp2dRm4NdAq+wgOLXak1Kf/otbg7ZmfvtZJ7TITH19kVf8alOu3M3mAUvB+F6pXrsUw9+doDZCYal67QofUGUunIGDHMEoXacjsS3oSUs1nVptMxAQe47sqMsrO9ElTX/VeVIPyjhtwj9SjTtfqUWWEROVJRhavT6kiBen72y3CkkDtPoOKlfPz4b+7sdwjgueA4R9IeX6XCnyUvyojd4da51w/Zse42hRvNpzV3tIM9MCdo2lvV5O+6/2Tt18bcR7X8QMZs1IqYyRfTyYwJASzf9V8RizzSDUoUs2ZRd2xKzmAXQq49fuPtPyqGXP9AZ5yaHpHSlYczmGr7dQPDjGq7L+RfVWLSWelOz/owny65KvMyFPS9zy0SfmMGY1OQ+iZ9QweKw0etH1UPiuo/Wllxp+J51HEn3xXqCi9N0WiARLjj1gHej3kiQGSDOHsGWjPVw7D2B5k+w0WvLdNZeiqE/i5L6N5kVr8uPeJ9PJCAqdHNOVTmQy7ae7csdaUF79EyhbMBOwYSouiMmR1Z7D5pjpt6tp2/KK8We0yG5fdd3mWvCePWN2nNlWazuec0cIpm5TDAVR4/TKciv+R8c0PYqBXjqAyg/LxZTY6OzNlnRcPzalGP/4Ncq2tAcerP+ev3aSyg0O2jr4+NKYA//6kGLU79KJPfyd693WiVxoms+lKNHHcZpbd/2g76ijXlQA+zLb/jFZDdnM5QXrIq5/dYsuIFtjXaUTbfk707+9E68oVqdtmCKs8Q4k0glF+h73Tu1Grdh1a9HXim/79aNyoKc3HbuNCZBy+GwdQK38BKrWfz9Yz3rhtm0yLxk1o0seJfv2dGNC1Oc1bD2XSIT+eGNJGYiaUuC3vTrPCLVkdbObp2WV8U7sQdi2dGNC/L05DJjH8xx9pVPwj7Hr/yi2DGd/D42j3iR3TLlqn5JggUavgyq+T6Vzha+o5OdG/9wD6NulAk/Z1KFh2FHtiLGhMJh5f/pUfm9lh17oPvfo70auHIzW/LE+3Gds4G60nMmIPg//3X5w2PeFJBlJWGkGZ9Jh1nT7j82J1aTd+H95RJ5hY8n90dpE6ehksoLZcYWqp/9J+/Bn85FGcmFKHivYNaNXTiQH9+zFo+GJ+6FKGL76uxZg9YcTzmJUNC9Gsx1rc46QygIkw1rQpRtP2izkWmzKAUaEDhQn0qhAOz+5FnRo1aNbfiQGd29GpURUKfFmYcj3WcD4LOnrJ9WAwJxHmvole9e2o0tWJAb070a5+O3otv8A9rZQejQm/zgaXEYzaHUiCTuoUpVU85eTkBsg+/ZYtTwxSfTP1ta03c/fsWgb1a4hjT+na6dXXibHzl7HrdiwJSS/WL5VGUJmi2Nrtf8hk9kzyiCYOMAVuoFk+GR81WMqJRwap+QQ3mFnxU1r/aJsS5c+C6vlpOXA3180xuDYuSPM+G1/s6KW8wNQmX1G4aEnqthtC1/5ODOhYk4r1WzNyz22irHPRtapQjizoR70a1Wjax4kB/Z3oVL8m1at1xWX/DUJTb78RdJonXP3FhQ5NG1HPur8DuremVfN+DNt5k6dAmMfP9CzyAYXrj2bOXn8CvbYwrGEhSjeT7o9+g8Yy/MfRtCwlo2SnNVxUWIi6e5gRdjI+d3Bi3JYLXLu2hO4fF2LEMROJRi3R13YxtmtT7Nr0oaeTE059etK1Xgs6zj7ClXgD8c/9WNhQxidlmtFz3jFuPNrD9198Qp81oTxKkjrWxd07w5zu1SnfpBPdnJzo27cDFRp24oeNVwiPDmb/6OqUrdqMdr2kZ8qQ4YsY2u5r8hVrxvSjEcgJYoHD57Qaso+b5qf8XC8/LZy2cy5eCnZ0D/9kYJXS1Bj6K5cVL6auFUbQyQM5suQH6jduTou+1uPXqSlNOo5m1qlgFCjYN6IOjSp/w/aH1kYqzwIZ2asDvZwGCynnBCoTKCNv86frOn7eG8AT0+tL2WCCyzf86Nt/4Ev5ZuBAWrRqQ7UaNVGbsmFBCj0odRYCz//KmhWLcZ7hwqRpGbGafb7PeWZM4NLv29h/7hGPNaA1xnFx93b2uYfwQC0NmFEBhlgffl8/nynTXJg4zYWps3fjFmZAhzSASAmYEu5xaPsiplq/Z9oGN7yskXrsw+sc2LQUl5lb2X1TDajwPraZadbtmei8kA3H7hEN6NKNvtYY9QTfPM3ebX9x7TkYtXJunNyEywwXJk6bxbKdN7mXqOLK7uX8tPE0PkkQEXKFA5v2cC48pcuTLVsSdHIR06e5MNF5EWvWnebcznEUKj6Vg4lmqRuXBSJv/sGcmdZjNX0O81Z5EYYU2cUnBHNi01aO+SXyTJdBS0Frm8LQa7+xcvE8pq5y51bMAzx3bufQlWipN7ARVMZozv22lQMXwqXoIuIq29bMZ+p0FyZOW8XB+yaeBLuz/aef+PVCBNFGDdcP/MbeEwGEJErTxjRGJdcP/87eIz7cU6Vrh2ptVmFWhnFix1KmTXNhovPP7Dnszv5Dx9hyxI8w3YsSyxRMoNVrCfLcwgxn6Twt3OTJfesiBolGeBZ1l73rlrL45ANJykZQqlX4ue9lzeYr3E0ySw1AUv1epVFKgYacWc5M67U9eeZ8/gyS0rwZLnhi7eUc6v07a1cd4dLTJGlOuSKUY9s3sdU9lMcqaRS9yhjLhV3b2Xf2AY80oDU+5/Ifv/DnmRAeG5K4fuA3/jx5Vzr+qerWSZpIzv75K/uPe7B/zx7mTnNhovM81p+6z1OzFFXbmoeYVI85s+sn6XxMc2HKzI3suRKPGtDqUm2/Vuobj0XNHc8dTJ9uvVemz2PV3luEY+1C9ewJ5/YuZ/aslaw6EY7cqCXg7DZmuriU2nNsAAAgAElEQVQwcdpMFm25zJ1EHTf2r+An17+4JgelUsn1oyuZO2cJi/f5cO+pL39t2oFbqIkEo3QsE0PdmDPL9ryYyYylB7mltta4k0zcPbeeRfPn4Lz5EgGx9zm9bRtHfRXEaqXnnBpQBp9i5aJZTJ7mwqRps3A5HMJza0pZ/uACG1bOtV7v6zj2EMLvHmPTkpXs8oohxqjk6p5f2OceRrhBjde+X/jzdBBhammcAkmXmdxtJFPXekkDGdNfx1qQmwFjDB771jI1+VmzlF88HxMHGIwa/D0P8seus/jGS887gIUr1+HYo5eQco6gBYUxJY32JulrnQluBtxnwpTpL2X69On06utEterZJGUrr+rKZPsYAaVWSuEnrxiU+u+2NKVWqr+mTwrYfj75OL6kLZ9cK6U0bV8zIf3ejFb6UetefKja6tMWm7AzaCGYZEppl6fRpfx7ehPSQ9OgIejib8yetpKLqfOzlhssbVSFSgMO4pdkkeqc+oyPX5KeNCOAba03X+ccaAzS9qVu7ahId+yV6Q6wiZTfYbEeay2pumppU1pvWrCmldNfv6laENo+ZlLOZUbHO9PuK/2L7Q21hpRzqjSmS19r0x77pFfUrdOn2P/uXKQ+H3oTyfVG23FJfTzTXP/atCtqaa1/Tt/VzNa+0sKL90Ca82I9H+nrnLbS2QvnwlpXfnEqT8rvVaZro6rM6LibUrWT1fNCG0619RqxlY1e1vZUZ0o5T6nPgcaY9p5I3TY2/cfW7jT977d1+QJrh7Z0xz75z9Z7ieB1DBn7E+vOy18+a+ZldWVSnndJWFusmkCni+bqflfsa9ZjwBAx0Oud43XT1xeu3aSyffakrwUZoAeNOYkn17YzrllpijYbwjeDhzBoyLf06t2MnoPXsT9Ii8bwN6vjCASCnMUAWlUQe36aRM+mHflxqyd+CZnUv90AOvMzfPfOoF4xGf2/HymknBfJqfWUBWmRG8BgshDt9xdTJ09k1LiJjBo3iYnzXHF7xouDRAQCQe7DAFpVCAddFzBmxl7OPdKhg+Ruev8UuRGwJDBvdA+69/1GSDkzST8l6nU+eWH0teAV14Qu46ke/2StaIFAkL3I9Snper2ZTBOyQif9LoBpc5aIBSkyG61JwbW9G3AeM5VJM2YyfvxYvv9hOENfigs//X6bx28w0Ot1EFIWCASCdwcQSzdmCVqTgluHNjCw2v+QyWQUqNaT7yaM5sdhI/k+Q2ayfJeQskAgELzPCClnEXKtNOLuedBxprSoTr0hmzif0RBGkb4WCAQCgRUh5SzENr1Bfmkxjcp/jt3w3dxRZH5/61chpCwQCATvDkLKWYzcKC0Zd2TzTCauOsi1Z9Kcvez694WUBQKB4N1BSDmrSTd5XGPI/EUnXoWQskAgELw7CCnncYSUBQKB4N1BSDmPI6QsEAgE7w5CynkcIWWBQCB4dxBSzuMIKQsEAsG7g5ByHkdIWSAQCN4dhJTzOELKAoFA8O6Q56Scel3PxIzWd33PEFIWCASCd4c8I2WlAeRJjzm1YR1LVm1m7fmnKFMJWqXPosXUczlCygKBQPDukGekrDKCIsmPzYP70biUDFnJ9oyevZI1G7fxp68yzZJ5ie+RoIWUBQKB4N0hz0hZoZOiZSMQd20zowd3p0PHjjQo/SGf1B3IlOW/8OvO3RzwlaOyyllnInPXwsyFCCkLBALBu0OekrINVaqo2BRyiNkTh9ClzlfIZDJk9v0Zv2gDm369jL8c1BZQ5mExCykLBDlLcobOKMa4CP6ePCllfXIh+QHHjh9h1959rPyhNpXqt6JNp460b9eYkh870HPZSW4rDGjysKiElAWCnEGpl/574OgpduzZz5NnStTi/hP8DXlKykoD6NFw++RRftu2jbUjGyH76EvK1W1Gq05dWeT2HLnV114Lm/Dvjzvg4hGBAkjMBTuYFQgpCwQ5g9oEcq2FgoUKIZPJuHLTH1Muf8gKcp48I2WlHpSGWLyOzKGF7EMKlK9HwzYd6TJhPzcU1lS2VcgGwBB6nPFDf2azVyRxZiFlgUCQudikXKacHTKZDC/fuxhz+UNWkPPkGSknmiBBdZqRBYtgV+t7Ngfq0VolrDFK0k6MDeXkSR9uRmpIsEjC0mXj2sY5gZCyQJAzCCkL3oY8IWXbdCcz15hRoScrL+vQ8eJHf24aJf6vHWNPhhELaPQ5v1NZjZCyQJAzCCkL3oZ3W8p6UGhN3Pc+zK7du9m8dTodC9egx4TVuP6+m1927mbrb7vZumM323fuZumQ6hQq8wOrrz9DnofryKkRUhYIcgYhZcHb8E5LWWkAZZIez9WOtGnakLp1y/OF7F8UKludWg0bUr9BQ+rVl6hbvyH1m7Ri0ObrhMjzftrahpCyQJAzCCkL3oZ3Wso2Eq31Yb0lkC2D5/D7dWVyi01jOgxILTdzemeyCyFlgSBnEFIWvA3vvpT1oLOOrE40gCWDWnL6j9aY8zuTXQgpCwQ5g5Cy4G14p6WsNIBcE4PXyTO4+ccRo37OrTNn2HfwCH8ceBne3I7RkmAAZS7YqaxGSFkgyBmElAVvwzstZZUJ4uWXWdKpPUPX3SA0wQfX7m2pV6s2NV7KRLb6xhJnEQO9BAJB1iGkLHgb3mkp21ADSSYp8k29GpSBVC03rR8T0ipROb0z2YWQskCQMwgpC96GPCFlpT4FNQZCrntzKTCeWB1oEsK5duUCR0+5c9QrkhgdaN6TkdcKnZCyQJBTCCkL3oY8IWWFDhR6aU3lyLDN9C1eivbzLhCckMDFRe0pUPBLylSqRuk6A1lwNoRn+vejcYhCJ6QsEOQUQsqCtyHPSFlpBKXmGTsGF6Ns52WcfqpH//AX2hf9mI97/Mat5xC4oQVFOkxnb0AierJnoJdcByqzlEo3v4Ss3BYhZYEgZxBSFrwNeUbKiSZIUB5nTPHCDN7wgMd6SDgxkjIFCtBt0z2eGMB4ZS72BXsxxe0hsYA6qzfaYJ0XHReO14ULnHC/wEmPtJw4e5NbD3UkWadqyTN5G4SUBYKcQUhZ8DbkKSnLVZeZYf8lnVfeIdxs4sSYMnz+7y4su5WABjO+yzpTKH8vFlyMJIEsHn2tB5VKTljQdTbPHkjryvY4ONhTuUoqHOxxqNCAjkN+5s+7j4hUgjqT0+pCyoKcRmmARKNUXlIZpT8r34PykZCy4G3IM1JWGkClNeCxqhllO09k04GVfFPoSwo1WszxiEQigncwsFIlyg3YjlekAaMlazdWDUSfW0GvGp9SqMsiDgZLjU1SD0pTWoB4XzYMq85nVboy0y0RcyafBCFlQU5gE3GiCZ6rDUTGKXj6TCIyTkG82kiiKecFLddJ94SJjDH8g/tRSFnwNuQZKSt00lu4UneT5e2bUq1yFSpV6cuyS3E8s8g5OLU9dUf/wpUIM6YsFrJCBwZ0nJ7bgdbtv2NjgHSgVcYX0QDaoL2M6NqOps6eRGdyBC+kLMhOlIbUIlYSI1fg4xeAp+c5vK5fx+v6dTw9PfH1v0uMXEFknJJ4tRFVNnfZs8nYgvRicO1WQIbcDLiPHqlF75u+PAgpC96GPCVlhU56KCRZB08ZrQOpbG+8tr9rsuEBYCKW3T/Wp107Zw7HSAc6w3oxwLNzzHHsTLVv/+Q+oMnE7RBSFmQXKoMk41iFilu3/Tl37hxe3t5cunyF0PCnUmRsgtBHT7l0+Qpe3t6c8zyHr/8dFHpjtlybNhmbkf4bEBjIn4ePIZPJMuTLLwtxwesW/vdD0L2hnIWUBW9D3pKyHhLNEBsRxo2bt7l83ZeLXre4cO0WF7x9ueTtw4VroYTEG5Drs3b0tQ4zV1370qJpW6adjEdvfSEwpANMPHFbRMcWLei43CfTl5QUUhZkByoDKLQGfKwyvnz5Cg/Co0g0WZK/x1a2sf090WThwZNI3N3O4Ot3B4VOSmlnRTo7tYyVerh77x57Dx5Jlm/+/OXJ/4XEFwXK89nn5ZDJ/pv89QIFCnLh2i3uhISg5fXuIyFlwduQp6SsMsKzyIvMa12G0iXLYVexIuUrpGckG27FZHmbTZUF5P77mNilLJU7T2L9aR9u+vhw0TsVPj74euxhar862LUfw5ZbZsyZnFoXUhZkNTYh3/L1w93dnYdPo5NlnF7ENpKb/ZhJJeYAnikTpXR2Jl6ncl1Kw6Cg+/fZve9Qsmzz5S9H6TLtmD7TgssccJ4Fs+bBmPFJfFV4OfnyV0Am+yRF3l/k56K3D8bXELOQsuBtyDNSVhpBmaTgzxFlKFLIidU+scTq9cSr9DxLg5F4rSVbNlipA70phuNLR9DCrgJ25TPAoTdjfvHjmdGSJS8JQsqCrES6nowpQo6IRm1+s2hXbYaw8EguXbrMuXPn8PULyLR0tlwHaiMYLbB732E++ECS6+efl6ZU6bY4zzLjPFsScmpmzJb+O2aChq++WsHn+conR87583/B5Ru3Mf2NmIWUBW9DnpFyogkSVGcYX6oQTSd7EKiS6j9q04tk23rKeuvoUq2JOKWO2AwxEK+XauBZ0URESFmQVSQL+bZ/coSsNr/F77LejyqjNZ3t7oaPXwAKreEfpbLlOmn8iNECu/enCLlM2Q44zzLhPMucLGHnWS/iMgdmWL8+bpKGgl+uQCbLnyzmS96+r4yYhZQFb0PekrLSnQl2X9FjRQAPkkCXS3pc20akZvSCoDZDolpJ8L0H+D1UEvcGtW65TpK+TegZAXDm3GUhZUGmYhOyz20/3N3dePQ0RhLyP5CoUi/dEw+eROHu7sadoGA0byN5XYqQTRbYve9QspDtyndhxmwTM+e+XMYZyXn2fBg3Uc+XX65MFvPn+fK9UsxCyoK3Ic9IWWUEpeYRvw6x56ueS3B/JO2cIQNRqXORmBItoAo+ikv3bnSdfJJgy+t3GlPpITJezc2A+y8l6P59Nm77Dfuq1YSUBZmH1iAJ2c0tJULOhAyUrc4cFPKQ6zd9UOpMb3zN2lLWJmvK+qOPPrQKuTMzZhuZOff1ZJye2fNh3CQ9X365PFnM+fLn5/KN2xmKWUhZ8DbkGSkr9JCoV3Nv+xAKfvYRjZxP4OkfQkBQCD53UxPJY4Uxy0dfvxFJRmLlGqKVxjdqs2m2wAn385QtZ/dS7OzsKFK0KDVq1kZjFlIW/HPUZvC/ew8PdzceRsSgzuQR02ozPIqMxd3dDV+/gDcWs0oPGq2O3fsOpQi5QhecZxneWsg2Zs2DcRN1aVLZ+fLnzzBiFlIWvA15RsqJRpBrrrG4eR3KFi1CkZJlKF02I1ENZ/3NrB99/UborentN5w/rdTBc7WRiDjVS1GoVPx56JhIXwsyBZUBFHoj3tdvcv9BeKYL2YY0XSoKdzc3fP0DUOiMr/Vzcp30UPO87M2//vUvZLIPsavQhRmz9f9YyGnEPEnPl4VSp7Lzc9HbF7XRlHw8hJQFb0OekbKEkZjniUTFJxGj1BKr0BIjTyQiVk7Ecw1Rz1U8iVUTqzHn+M5kFko9aMwvB+CkxwUhZcE/RmkAhd6Ej58/Hh7uhEfFoc6icRtK64uqTcwBgff/tr5sS1ubLbBz7wFkMhmFv67FjFm6TBNyejEXTJXKzv9Ffh48fZbcmlNIWfA25DEpQyLSqOu4qFhCnsTxME6P3AhyeTxPNVK3L002iUllkv497WuiMYnR14LcjBEf60jrR0+jSTRm7dRCW335Xugjrt+8hUpvkl4MXvL9aiOYzBb27D/Mfz7+DzKZjPIVHZmzQJrilJlSdp4Fs5JrzCv44IP8fPSRjEvePslTpYSUBW9DnpOyWp/Ew8cncG5Um7JFC5Gv8QIOh6q5vLEdPaZvwz3MgNac9fVkpd5ETEwcIQ+jCYmIJfjRUwJDn3D3pUQSFp1EfCbXuoWUBZmB2gx+d4Jwd3fjYUR0ti0koTbD48hneLi5cdv/DkqdKcPvU+pBq9Oze99h/v2ffycP7HKepckSIace/DV2op6P/zsHmewDvvjicy5dv43GaEIjpCx4C/KOlPWQqDcSdXwGNap2ZcKe25xe3Ytijaex464KpeY8M+va0WDoLq5Gm9Fl8aIUBkssJxb9SIPCBfjsf/koVLQMZe3KUaZMOcqUzYimDJjp9kajr18HIWVBpqA34X39JsFZWEd+GSqDhQfhUtevgMD7Gc6FBjjjeZH//lfqvlXOrhPOMzXMmpd1QnaeBTPnwtQZ8GWh9chk+ZDJZHzxxRcEP47CgpCy4M3JM1JWGUGReI/1PUrTeOJf+DyHB7sHUrqlMzvuKNEC97YNoEyBXsw+/4Q4Mld+6VHqzShV8ZxZ9Q1VPsxPg2G7uBgZS0REDCHhGRHH4zgdCWI9ZUEuQ20C/zuBeLi78yT6eZbVkV+Gbf7yvdBHXL9xC6XBnLaHthEsFtixex8ymYxixRriPFOd5UK24TIHxk7UUPDLZXzwwefIZDIuefsC0rYLKQvehDwjZVvzkIl2hRiwNphwHQT/6kSpFs78ZpWy5vxsKhfug7P7I55lsZRt26Q3aTi/0gn7+h0Zd1R6e9Yi1ZrTI2rKgtyG1KPajJf3DYIfPMnyOvLLUJvgcdQz3N3duO1/F4VeSmMrdaDTG9iz/zCffvYZH374LypW6sHchVlTR37VwK+xE3X895M5yGQfUaBgPq7fDkRjNFHWrryQsuC1yTNSliLlINZ2L0WTyUfxjYewXQMp3Womu4N1GCxaTrlUpWid4WzxlaMGVNmw0SrAEn6KcR1LUqb3Es5EgeEtuxS9DULKgn+CUmfC1y8Ad3e3HImSU6MymAkLf4q7mxv+gfdINEkPsONunvzv00+lKLl4Y6a7KLJVyLZoefpMKPhlShr7q8KFCQx9jEPVakLKgtcmz0jZVlOOPDaNag5dmbjXH3fXPhRvNJ71V8K5tWsENUp9SqtFl7irtGRrC85EnYno2FhCIhXEaLK3HiekLHhb1CbwuxOIh7sbD55EozLk7FRCpbWX/P3Qx3jfuIXKaMFoNPLLrj+TV3GqULEH8xZnb5RsY8ZsGDNeTcGCP/HBB5/zgUzGhWu3qGLvIKQseG3yjpStqHUawh4dZ3qjOtgVLUS+AoUpWqosJb+ux7BfrxOYaEZjyOZuXnpQW0BrycbFMKwIKQveFqXejNf1G4Q8fJJl6xy/KWozhEfH4e7uRnhkDMfOeJIvf34+/PAjypbryNQZCTkiZBuz58PYCTr+9+k8ZLKPKVCwIP/3f/8npCx4bfKclG3zlGMjorj3KJrQJ1HcC3tMQEgMkRrrKlG5YGeyCyFlwduQaIuSPdyJiE0gMRtLLn+HymDmXtgjQsIe8cfBo8hkMkqUbM40F3m2De56VRrbZTYU/HJTchrbhpCy4HXIM1JWGiXhPk9I5GmcmmiVnrhEA3GJBp5rQQfoLbmo33U2IaQseBuUhpTBXcocTlunR22CiJh4/Pz8WL5iBR988CEVKvVk/pKcFXLqNPbo8YkUKLiYDz7IL6QseCPyhpT1kKjXEf3MnVmN61IpXb/rMm3m82dALM+NkJibFqLIBoSUBW+KygS3A+7i4e5GRGzC262RnIUkGqS5y+FRz5g6ZTSFvqrPlBlxOZq2ziiNPXq8ls8/X4BM9l9kMhk3A+5jyuUPWUHO885LWWkEVZKO43PqUq1SEXos8MTTN4jbd4K44R/ErYAgjq8YSG2HUjRwPoFfLOhy2UMmKxFSFrwJSj0oDRa8b9wk5GE4Sn3uipIVOjCazOz76zglS5Xis8/+R/kK3VmQS6LkNGnsOVCo0BZkMmlk+OUbfpjJHbV5Qe7l3ZayHtR6I9FnFlC1ZC2+2XCBgERpDWXbOsoGQKuO5c7ZpXSsWBann28SogGtKed3KDsQUha8CUq9BZ/b/oSFR6IyWnKdQAD+On6GAgUKStOOvmrEwIFBjBrznBlzcl7G6dPYo8YqyP9FOWQyGUWLFeP67bvozTkz11vwbvBOS1llAoUqmI19y/B5tzVcjpIklD49rbKA0aLn8PiyFHX6idNhhvcmjSSkLHhdVAZpcJeb2xme5sK0tUIHFouFrb/tRiaTUbp0a6bOiGHCJD0//BiZq9LXtmh55lwo+GWl5LpykSJFuR0YLGrLgpfyTks50QzP408zpkwh6kzx4L6CjOcfW5eBu7ejH6WL9Gb+hafEk4vWU85ChJQFr4vKANe8rxP8wJq2zmVRsgU4dPQkhb8ugkwmo1KVvixeDhOnJPHttw8YOSp31ZWlhiIWChSsmGYU9kXrSlI5fTwFuZN3Xspxz48xumQRBrjeJ0wDScYMflhv7fjlOQf7Qn2Z7v6QWLK+zWZuQEhZ8DrYomQPD3eePlPkyigZYPMvv0tRcpk2TJoWyYw5MGO2hTHjFPzwQ+6Kll8m5ZKlSuN7V0TLgozJA1I+ztjSRRiyMYwHSa+WsvriIhyKDmCGR/b0vs4NCCkLXofkKPlhOEq9KVdGyQePnKBIkaLIZDIqV+nHop9S5DdxqlaKlkc/zzVifpmUZTIZF718MOfyB64gZ8gzUh686SGP9dJc5ERjOkygsUDSpYVUFVLO8W0S5C7SRsnyXBslb9y+0xolt2Xi1Ig08p0xy8LocXK+z0XRsk3K+fJLq0T9efg49g5VrftQlpsBQZiyeAlZwbtHHpDyMcaULMy360N5kGQdVa1Ph0ESs/rCfKp87ZRtq0TlBoSUBX9HohGueUlRskJvynUjrs3WKLlY8RJSlGyfEiWnFqAULYcxcnTuqC2nl3JAyGPqNWiYHC2fu3YTSy5/6Aqyn3deyvHyU4wukp8Che0oX9meylXsqVQ5HVUkKpf6kv/7oCvzL0UIKQsEOul68L8bhIe7O5FxylwbJW/YtgOZTEaZsu2ZMOXxS6RrZszY3BMtp5fyDb8ggkNDKWtdX7mcXXlu+gdhFNGyIBXvtJSVelBoVdzzDeD8pSuc8bzEKc9LnH4Jpy54c8H7AWFyA/L3pLOXkLLgVagMcM3Lm/sPwlHocmuUfJwSJUshk8moYj+AxcteIsG5MMkWLY/K+dpyRlIGqGJfNSVavnoz1z94BdnLOy1lG1rA9AZkOBgsjyKkLHgZSgP43Q3Cw8MaJZtyfpvSI0XJvyGTyShbtj3jJz/A5ZVNQszJteVXf1/2S9nb9y4W4FZAIGXtyktLTVaqjO/d+2LQlyCZPCFlwcsRUha8DJURrnp5cz/sMbkySrbAgb+OUbpMGWQyGfYO37B4+atFOHMuTJqqyxUjsdNL2csqZYAqDinRsl2FitzwvSMGfQlQ6ISU8zxCyoIX0EulH39rlBz1PPdGyWs3bZOi5HIdGD8p7DWjXzOjx8r5/oenORotZyRlE9I9eMM/gNJlyiaL2eOid65/AAuyByHlPI6QsuAF9NKI66vXvAnOpVGyyRol2wZF2TsMZMlycJ759zJMHS2PysFoOSMp2xqGSGnsO5SvKLXgrOJQleu+AWLQl0BIOa8jpCxIgzVKDrh7Dw93D6LiE3NdlCzXSQ+mNRu3JkfJYycEv1HUO32WmdFjE3I0Wn6VlFUGaR+rVq+REi1f8gKk/c/pcyDIOYSU8zhCyoI0WLvbXb2We2vJtii5nHUwlEPVQSxZAdNfI0q2McMaLQ/Kwdryq6Ss0EnZCi9fXypUrIxMJqNqtep4+/iL2vJ7jpByHkdIWWBDaaslB97Hw8OD6FwcJa9ev8UaJXdkzPigt4p2p9tqy9/nTLT8d1K2RcvVqtdMjpbdL14T0fJ7jpByHkdIWWBDaYuSr17jXtgjFFpj7oySDx+lgrXWWq36UJa+YZScOlqePE2KlkeNjs/2aPnvpKzQSdHylRs3qFS5CjKZjBo1a+N9yw9jLmziIsgehJTzOELKAoUuJUoOCLzPWQ8PouPVuTZKXrlmQ3KUPHpc4NtHubNT1ZZzIFp+HSnbouWateokR8unz10R0fJ7jJByHkdIWaDQSUJONMKVq9e4F/oIuTb31ZKNZjj419HkqLFa9e/fOkpOEy1P1zNo0ANGjcne2vLrSNl2Xi55eVHRut+169YT0fJ7jJByFvOyB58tcsnqB6OQssB2jQUEBXPWw4OYBE2ujZKXr16XHCWPHBvAzLn/UI7pouUZ2Rgtv46UFbpU0XLtusnR8inPSyJafk8RUs4i5DpQI7X11BhS3VwGqS2oJRUGQJVF2yGkLLDVkt+FKLlyFQcpSq7xI0tX/rMoOVmOc2HyNGu0PDoe52yKll9XyrZo+fyVq1SqYo9MJqNu/Qb4B4UIMb+HCClnETpzAmfXOzN6zFqOhIHBDAoAdTC7Fw+lSbMWNGnWgsbNBjBlsx/RgMGQ+TegkPL7jU2+d4KCOXvWgxi5hsRcGiX/9LOrFCXbdWTEmNv/PEpOFy2PGpPAd9kYLb+ulBU6UOilY1CrTr3kaLlm7bp43fSVnh254DwJsgch5SzCzFN2DKxK6ZJ9WekjHWjNY09+njmC7qMW47p9G+s3bWPb9m3MGTGUyZPWcf456DL5gSmk/H6j1Evn/MqVq9wLeYA8F464NlijZIeq1aQRyLVGsvTnzImSbcywRsvfDnrIqDHxuU/KOlAbwfPiRUqULJ0s5qNnzolo+T1DSDmLMBHFnmGNqeYwlHV+AGouLetLq7r9WXzDWkSyfiKOz8KxQSu+2RFCYiansoWU31/SRslniVUk5dooedHyn1Oi5NGZGCWni5ZHj03gu+8jsiVaflMpK6zH4uLVq8nLOzZo1IRrN0S0/D4hpJxFpJFyAEAIa/tXoW7TSRyOkVbAUehAYQGi3Znu2JFqg/YTAmgycTuElN9fbLXky1euEBTygIRcGiUfOnIsud1kzdqjWZbJUXLqaHnKdIN1JHZCrpSyLY1dr2Hj5Gj5yKmzUrSszfnzJch6hJSzCBNR7PmxEdWrj+CXxwCJ7B/bmFbNJ7I/kuRWekrA9OgYE3t2oN6YE4QD6kzcDiHl9xmWzzAAACAASURBVBObfO/eC8Hz7Fme5cYoWSs9gBb8tCK5x/WwUbcyP0pOHS3PNDNqTHy2RMtvJWWdlMZ29/RMjpYbN2uOl4iW3xuElLMIM0/Z9X0jCsvyU7ZBJxx7dqdx5a8pVn0oy64nSW++1rfioB0jaVK+Kd8feIYRUGbqdggpv4+kiZKDHyBPyqVR8tHjVKshtZmsVWdslkXJqUU5xdkaLY/N2mj5baVsi5YbNGqaHC0fPuEuouX3BCHlLEJtTCLI6xy/b97CetdZ9KleyHqDFaDJpCPcs4Ah/g5/LO6LQ63mDJrnQaActMbM3Q4h5fcPSb4W7t4PlaJkpTbXRsnzlixLjpJ/HHGdWfOyNnp1niVFyyNHxzNk6OMs7fL11lLWgcYIp9zcsXeQouXmLVvjdcMHfS47j4LMR0g5i5BrQZNqMNdT/yvs2forGzb9wh9udwk1gFYZwdkDm1n6+3n8VGTJm7CQ8vuHbcT15StXCAwOy5W1ZL0JDh89Ts3aUnvJOvUmZnmUbEPq6mVh7HgFo8fJs0zM/0TKtmi5cdMWydHygSOnpGdELjh/gqxDSDkbSC9oC5Cok9LXulT/3wyohJQF/wClHhJNEBgcxtlcHiXPmr8o26Pk1MKc4mzg22/DGJ1Faex/JGWdNVo+c4YSJUshk8lo0aoNd+6FiDR2HkdIOYdRmUH56CJrJ01i3KorPDRLwn6dn5VrQWPhbz/nr96gsoODkHIex5a2DgwOxe3M/7d333FV133jx09X131f933dv6ulluZIUdwCaq7cgrgqtTQr994jW25xpllpmZWalpWV4sitOUAcCCqIoiCO3CKgZzDOPq/fH99z4IBQiAf5Qm8ej+cfAeGXzzmc1/l8vmuXcsS1Cvclm22weWvWLLlx03cf2SzZXeZBX4OuMaUQrvL1sFHWmZS/37379lPHeQ53sxatCI88IcvYJZhEuYjpraC/dZoN36xg6YazXLfm/zxlix0OHoui22s989SjZ0+at2yFr18D0mwS5ZLMtWwdFhbG2fMXVfkmzDVLnjJzNhqNhipegQwedvSRzpJdJs+A9ydZ6df/cqHMlj0RZdcydut2AZnL2D9v3Cqz5RJMolzUjKCzgtU5q32Q5WujFU7GJjBpWlCeZgYF8VafftTz9SPVqr4XaeEZejPojA7Onr/I/v37STaYSfXwQYOeYLbBlm07aNhIuZxkk2YfFMksOdtsefRdBg685vE7SHkkyibIsMLWbdszr3jmH9iR8AiZLZdUEuVCpjVC6l8sLzsKuC9Za4SM/CxfHz0uy9clnN4MBjMcDAvjbPwFVS5bu2bJk6YHOWfJHRg07EiRzJJzzpb79/P8bNlTUXbNlv0DO2bOln9ct0lmyyWURLmwGEFnUwY48fg6Zg/pQ8+3+vBGDj17f8AnG6+QCthscvS1eHB6M2iNDs45Z8kpqeqbJbuCvPfAQRo1aabsH20+pUhnyZmz5Sl2Ro5O8fhs2WNRNoHRBr9t2UqduspsObBjZ2LjL0iYSyCJciHR2yD1+kl++ngUnV7pSq8BHzJl8odMeM/NpA+ZMnIw3Tq9yhtz1xB6FSweXpKSKJd8rvtyHzwYRmz8BbQZ6rs1o8kG23bspJbzKlXlK7zEoGGHi3SWnG22PNlK/35/MHqslknTHB65vaMno+yaLbfv0CVzttyqrT8Rx096/CY2omhJlAtJBhC3dhRt61WgxdS9nDfntbis59AXPfGq05Le313BhFzRS+Sfa5Z89vwlDhw4QEqqRbWz5Hc+mOJctm7PsFFRzP6o6GfJmbPlqW6z5RmoL8om5RiSzZt/o4LzFCmNRsPqn9bLbLmEkSgXEita1o1pTvtO77PhNrn/4RhR7rGcuI8p3TtRf8hGLiLXvhb5l22WHJegyn3JJhts37mbxk1fUq7l3HKmKpats82Wp8P7k23073eZMeN0Hgmzp6PsenOzdduOzFOkOnV5lbPxcu5ySSJRLiRWdKwf14L2nd5j3Y2/iPLtvUzu3okGwzZzSaIs8klvhlQrxF24rMyS06yqnSWPf3cSGo2GylUCGDD4INNnFX2I85ot9+lzkdHjtHw47eHC7Okou49n51e7Zc6W2/i3l2XsEkSiXEgygPPrxtGmdlnazQnjRl6r16Swd0EXytdsSf8fr2OW5WuRT1qjg3MJl9mze5dzlqzOfck7du6mifNWhC1azVbdLNl9tjx5OowclcLAgVeVg75UFmWdSVnG3rBxIxVfqJwZ5uWrf5TZcgkhUS4keiuk3TpN8Bcj6d6lGz36jGbMmNEMHeFmzGjGDh5Br35DGPvZOg5dB7PckELkgyu+oaGhnDl3XpWPrWtWN3r8u2g0Gl6o3Jb+g0JVOUt2D/MHk2307/8HY8Y7Z8sqi7JrXH/bui3zhhVdXu1GxIkoj9/QRjx6EuXC4nZKVMLvPzB12HAGDBnOwBwGjP6M7yLSAOXIazklSuSH1mgn7sJlQkJCuKviZevQsMM0fakFGo2Glq3nqXaW7M61jD1g4FUmT6PAp0kVVpTdx7fr6z0zZ8tffbtGZsslgES5kGmNylL2n33YQYl4Ifz7EuWSJ+csWY33SjZaYc+evdT18UOj0VDphTb0HxSi6llyttnyFGW2PHa8jikzChbmwoyyzqTsGvh13frMZeyXu3aXg75KAIlyCSdRLllcB3fFX7yizJLTbaqdJQ8bNT5zFte67UcsKAazZFeUP5xqZ+SoZPr0vcjY8TrVzZTdx3nT5i3Udi5j+wd2Iu68XFSkOJMol3AS5RLEDFqTnfiLV9izezdnzsVzz6i+g7uMVtizdx8vNW+JRqOhbLlG9Bu4v1jMkt3DPHk6jBiVzIABVzI/p6Yo60xZYe75Zp/MN0D+gZ04cVL2LxdXEuUSTqJccuityhHXIaEhqj+4a9CwUc5l69YMGR7JHBVdKORBwvzBFHuBl7EfRZR1JmUZ++dffqVCpayjsT9b+rXMlospiXIJJ1EuGfRmMNgg/uJVQkND0WbY0atsJuQK8uGj4bzUohUajYZ2AZ+x4JPiF2RXlD+camfEqGT69nEtYztUF2XXuAdv3Ext52VMu77Wg3NybexiSaJcwkmUiz/lUpo24i8py9anz8ar8pzkDCscOBBCPb/6aDQanivbkH4D9zNjdtEH9mHCPHk6jBj54MvYjyrKOlNWmPsMGJw5Ww7o0Jm483LgV3EjUS7hJMrFX6oV7mXYCQkJ4fS5eAxW0KvscXRFod+goWg0GipWasngYRHFctk61xnzFDv9B1xh7Hg9k/O5jP0oo6wzKfeq/mntL1R0W8YO6NiZk1HRZKhsVUXkTaJcwkmUi7eso62dy9ZGh2qXrY+GR9C8ZWvnrQWXFdtl61yjnG0ZW09+lrEfdZQzl7E3bMp204p5iz6T2XIxIlEu4STKxZeybG3n/KWr7Nm9i9Oxccq+ZBUuW4cePEg9X9eydYNiv2ydW5hdR2P37/8HH07969nyo46yzpQV5nUbNlGrTl00Gg3dXu9JfMJFCXMxIVEu4STKxVeqFe6m2zhw4ACnz8ZjsKC6ILsi8Gbv/pn3SS4py9a5z5gdDHAuY//V0dhFEWX3x2TIyDGZs+XATi8TnyD7l4sDiXIJJ1EunlzL1gmXrxF68CBak3qDHBF5IvNo646dl/PRopIXZPcojxiZRN8+Fxk34c/DXFRR1pmU/ctrflxLhYovuIW5C+cT5IhstZMol3AS5eLHPci/795NTGwcWqP6lq3TrXD48BF8/Bqg0Wh49rn69B2wr0QtW+cWZuVo7CT69lXCnNf+5aKMcuYydvDGbPuXJczqJ1Eu4STKxYsryOcvX3Oe/hSn6mXrHr16o9FoeL58UwYNLZnL1nmG2bV/eYo919lyUUZZZwKtKY8wd+xCvIRZtSTKJZxEufhwnY+sHNi1m5izztOfVBrkyONZy9adX/me+SV02TqvME+a5mDAwLxPkyrqKOtMWWFeH7wp2/2XAzp04lRMDOnyeqA6EuUSTqJcfKRaISXVzP79+zkdq+4Z8smTUfg4LxJS5lkf+g7Yz4w5RR/LRxplt/3LYycY7guzGqKsM7nNmDdsynYpzvcmTwfALDNmVZEol3AS5eJBb1Yem/OXrhIWdgi9ScVBjorKPP2pTJl6DBp6jNl/g2Xr3MKcuX85lzCrJco6k9uM2S3Mr3bvQXBwMFFnZSlbTSTKJZxEWf30ZjA4D+zas2c3MbHn0Bkdqoqy+wy5nq9f5gx50NAIZn8Ek/5mQb4/zMnOI7INmZ9TU5R1Jvcwb86+lB3YUQ7+UhGJcgknUVY3vfMxSfjjurIfOTZOdfuRXS/m0dGnqOujBLl0mboMGhbJ7PlFH8ai5n7gl3JEthLmqTPVFWX3x3L9xs3ZDv4KCOxEwoWLEmYVkCiXcBJl9dJb3GbIag/yqVPUqecrQf6rMGeeKmVg6kzla2qKsvtjGrxxMxXdwuzfviMJF2TGXNQkyiWcRFmdsoJ8nT17dqk6yFFuQX6ubAMGDTsuQc6NW5j79LnI+IkZTJoOTz3traoouz+2wZuyL2W3DQjMmjGrYDv/jiTKJZxEWX2yBdk5Q05V4ZHWAKdiTlO7br3MF+3Xemxm3kIVBFCtpitGjEymfz/ldKknn1TXTDnnYxy86bdsS9nt2nfgwsVLWUdmq2A7/04kyiWcRFk99GZIszuD7LYPOVVlM2SdyRnk06epXScryKVL16b/wDBm/o1OfSqoydNh1Oh79OlzgX//u5Jqo+x6rIM3b8kW5rYBHdi793eiz13EIWF+5I+HRLkEkygXPa0J0mxgA+6mZRB34Q/nUdbqW7LWmZz7kE+fzrzLUKnStfGq5s+AwceZNa/og1dcTJmhLGX/3/8psTtz4apqAwewYfOWbPuYNRoNrdv6c+GChPlRPxYS5RJMoly0tCYw2kFvdrBhyw6++uorwiMiVHv5TICY2Fhq1VaC/EypmgweHsXchUpkijp0xYlrvFz7lBd+uZwMuzLGqVb1RQ7g141badOmDeUrVsoW5ouynP1IHweJcgkmUS4aWpPywgtgttn5eMkyNBoN3jVqkuFQlrGLehvdt9W5qcTEnqVmrTrZghw0t+gDVxy5zlN+ulQNNBoNtZ9+mmXfrGJ/aCh6E2TY1Re4dOebhs1btmebNbds3ZawsIOcipM4FzaJciHTmsBgV170HHmwAPpC+vclykXzmBvtyr2QQ8IOMnPRYjQaDXVLl6aujy9pNmW/clFvp/tzJCL6LCGHDuFV9QVnkGswZHi0BNkDUX7SOVOOGz2O/s5dAp9/tRKz3aG6WbPerAAI3rKTli1aUMFt1tyiVWsOHzpE9LmLEudCIlEuTFYlxvrES4Tt2c3GbbnYfoTwCxlkAMZC+OOUKD86WhOk2pyzY6uNj5d8iUajocGzzzKmcROO9ulHbR9f9FZ1RDlzdnzuHN41aqLRaHjiyapUqNiUQUNPSZA9HOWYEaNh3kLecoZ53ufLCDtyBK1RfXHWmZTjIAC2bN+VLcwajYZmzVsQHn40c+ZskTh7jES5sJjBoL9LfEwYn096G/86vvj5+VLXx42vL361XqJj3wX8EJXANR2keXgfo0S58LkO5AJISbNyOPwoMz/+DI1Gw0Df+jB7Hnz0MfvefJta9XxUEWUbcPz0OcKOHaNylYpoNBqeeroaQ4bHMGu+7D8ujChHDR8Jcz+CWXPpXdeH+mXKoNFomLvkS46Eh3Mvw45ZRS/CerNy+h7A5h17aNqkyX1xbty0GRHHjhEjcfYYiXIhSQNu7VtAd9//47muC9iiPGdJt7sB0J9m+cj6/KdmZybtMmD38IMgUS487jFOTrUQfuwY0xd8gkajwa/MswzyawCz52GdMQvmfsTeXm8VaZQtKM8HgFPn4vCqppw/++STVXm+fCOGDD/DzLn334JQeDDKs+dhnh4EQXMgaA59fXzxc8Z59mdfkHzvLjbn45RqU0ngzFnP89927qNx40ZUzBHnFxs3ISIyklNxF8G5/RLogpEoFxILRnbO6IB/xyF8E6sMtCuK7tIAU9w6Rr0aSIsP9nELSPPgdkiUPcv99CaAJIOZyOPHmeGMsU+ZZxnkVx9mzcUxczaW6UGYps2EOfOLLMpWXDPjeA5FnOTIiZNUeqG8M8heDBsZS9A8ifGjirJp2kws04OwTA/CMXO2Eud6PviUKcPEdyaw7UAYoRHHSUlTdjCkqSDOrruYuQ5e3LbnAA0bNKBCxYrZ4ly/YSOOnzxJ2LGTmUvbNue+56L+2y0uJMqFxEYSPw1rSmDgJDYlKgOd6x8WQNIBZnTtgk/fX4kH0j24HRJlz8lQdv1xR28iJPwkYceOMWXeQjQaDfXKPMuQ+srM2O6MsSvIRRFl16zYBkSdjedQZDSVKmddTvGJJ6tQtpwfQ0ecY+YcCfKjjrKL63linzkbZs2lbz0f6pZWZs5BixZz4uRJklMtmXEr6kDnjPPOfQepX9/vvjhrNBrq+fpxPDqaQ5HRmRchsbmxqDw4RUWiXEhM2Alf+iatm7dl4pYkTHDfk9IGgJU/dgTRvlUbunwSjQ4weHA7JMoFozUppy3ZnHGzAhdvJHE4MpIPZs3lHxoNdcqUoXap0gxv8CLMnodtxqz7YlzYUdablRc31/PJta3HT8cTfvIUh4+f4rnnSmWLcZln6zJ0ZJwsVasgyjnjbJ0eBLPmMtDPj7rOZe0ZCz/lcGQkoRFRJBlMmYEuykjnjPPuA4epW88XXx8fni9f4b5A16xdh8hTpzh6QnlOHj5+iqizF+57PZRQS5QLjcEButPBvNfVm9qdx7N4yzGOhh9jX5ib8GNE7PyBd3s1pkbnCayMsmN3eHY7JMp/TWtS9vFbyeIAbmvTOXQihpDjJ9kddpRRQ4dSu0wZapYqxagXG2GfNRfz1BmZL6a5xdhTUXbF130bbc5lwRNnzhMeFUt4VCyRMbEcjjxF6dJPZYX4icqUKl2L0mXqMHxUAjNmZ93VqKjDVZI9SJTd42ydHoR1+iwcQXMZ5FefmqVKUbt0GR7XaJg6fyHHoqLYe+wkh07EkKgz4nB7TqQ94nOf9WYlzAar8toCsC8snFq161Czdh1q1aqVa6Q1Gg1Vq3oTER1L5KnYzOdv1NkLmW8yrbmwoL4L7niaRLkQ6c1gJYVdi8bgX6Mm1XPj24vxa85yD88fea0zSZRdtCZId+T+h+4Abt5N5fDJM+yPiGZ/ZDSRp6IZP3ka/3r8cWqVKUONUqWY0LgptqC5mDJDPOsvY+we5d/feJM6vn7ZZrT5YQOMwInT8UREn810MvYsIUcieeLJf9/3gvfEE5V55pkaPP10DUaMvsx0txBLjNUb5ZxxtkwPwjR1BragOYxo8CI1nilFjVKlqFWmDP/9j3/w/sw5RJ6KZn9ENIdPnuHWvfRskXaXXojBdsU51Rln1/nOdiAsIooaNWvhXb0GNWrUoHyF3COt0WgoX6EiR0+e4cSZs9me61nP+QRMfxLtB2GwFP2++txIlAuT2fkO0gzaDBt3023cy+FuugOdVZnxFMZFRB42yq4ZWnHnAK7d0REeE8fhHGLi4hj7wWSe/Pe/qVe+PHXLlqNuuXLUKVuWya1aw/yFWGfOxj5rLsyZr5g9H/usudhnzcsXFn7Cwb4DqFq9BsfPxHHiTByRMflzKi6OPSGH+Ne//pHrC9kTT1Tm6ae9ecrpmWdqMWrcNaYFKXGYPF0JxGQ5zanYRDlbnGfMwjpjFubpQRinTCd98jSss+YyrlFjqj9TihqlSlOzzLP8zz//ydgPp3AiJoaQ49kdiYrlRrIh82JFBWEkfxFzD3TOSFsdyoVqqteoiXeNmlSt5o13dW/KV7h/n3Runnr6GfYfjiT6XP7/fvLietPwKF5/HuQNkUT5EdBbsj9J3aXZwZCq5dyZeE5c0JJszn+YXZdy/LMnA8DukMPU9fHNtt8mP6woBzdFn7vIydiEYi0hIYGhI0bzn8ceo/xjj1HOzfOPPcZzGg3ja9fm7rh3uD1sBLeHjSBx+ChSRo1FO2Yc2jHjuDd6HCmjxhRI6oSJ7O/ZC41Gw2OPPfbAHn/8v3imVG1Kl65LKTdlyzVh4vsGgubCtFlZZsyBmU7TZ4miMHMOTAuCp56p4byi11j4aBHMmlsws+dlvSmcM1/53IxZOKYHwbyFTG/ZCp/nnsOvXLls6j9fnmf/3/9j7IR3SUgo+N/QpZvJBYqYFSV+LjaUSYgLQPyVW/j41qeejy+169TNpk7dupkXtyno309u1q7fTNxDjMeDuHU3Ld9jBzB15hy6vd5TolwUUh1gOL+Vqd278ep7OznvyP8pUalmuJ5s4MjxmDxFx8Tw+Vcr8KrmzdGTMRw9mff35nQ8Joatu/fz+OOP5+tdbHFQS6NhpEbDgBwGajQMc37N3QiNhqEeMFqjodtDbft/8cZbYfQfFE2f/ln6Doim/6BY+g86m02/gTH0HRBD34GiqPQfFEPfAad4/L+eRaPR8EvXbsQMG8HxgYMf3oDBRA0aQvSgoYrBQzkxcDDhfQdwtE+/bE4MGc7rVas+9N+Of2AHYmLy//qRm8PHYzhy4jTHos9mEx4VS1hEdK4ioqPZvOP3In/teBgjx47P99jFxMQwcMhwerz5tkS5yBhtpKSaSU6zP9A+DrsDtu8NpWq16nnyrl6d58tX4L//+19U865ONe+8vzcn5Xu9qVjpBSpWqlSsVahYiUqVK+NVxYtqefCqUoXKL1QuPJWrULmKV4FU8apKVe9aVMuFV9XqQsUqe1WlchUvqlatRrWq1ahaSKpX86aGd/VcVa1ajUovVKZCxYL/Db1QucoDvX48iGre1alRs1aeqtesRRXnOHpKxUovPNR4PIjKVbzyPXbVq1fn6WdKMXDoCIlyYTHYlP0x5jxkOJzL2ZYH/9n3Muwk6ox5SjcaCd6yk1p16pJkMJJkyPt785KSZi0xktOsJBWhgm+7JfPxE8VLSpqFlDQrdwxmEotIUqrl4f92DOYHfu3wlDvF/HUoyWDK9+9qNBqZ+MEUXuvZS6LscRYlxgbdXeLOxnP8dC7O/EHCHSvpQLrlwY8G1DsvgZcXgO2/h1Cnng8ZDuUNwJ99vxBCiKIDMGlakBzo5XFmMKRnkHjrMsGLhhFYvRrVqlXDq6qbatWoVrkJr476jr03U0hJU47U9uR2yClRQghRfMjR14UkFUg+soy+TZ+hdPsPWR1hQKc3cD3Zjc6A/lIoC/rW5an6bzAv1Cg3pBBCiL8xiXIhMWNh3/xXaBfwJguPmrE59x1nu0uUA0yA7sS39OsUQNuZh0gCUj24HRJlIYQoPiTKhcTGHX4c2kS5IcUdZaD/8oYUfX4hjsK5IUWdej6ZB5YZhRBCqBLAlBmz5TxlTzNjJfTj7rRt2505BzOwocyKcz4AVhwkH/6CnoHtCJgTTgqFM1N+oYoX5y5d49yla8ReEH8l/o+bnL96u8i3oyQ5f+UW8VduFfl2lBQJV28T98fNIt+OkiThaiJxl28U6TZcu3aNoSPG8Gr31yXKnpTqgLuRqxjU8lm8u8/k56ibXLtxk7g/3Ny4yc2YXczq7cNzTfvxyRGLx29IYXPAwfCTlCnzLFW8qop88PKqStly5ShdpgxeKtieksDLqyrPPvsczz5XVsbUQ+NZunQZyj3/vIynBz1TqhTlK1Qs8jEt93x5xk58H4eHe/C3jrLOBHqjBb0unrVT+tKssvKHVDkHr5qvMPjzg1zQpnEvw/PXvtab4G66jT9u3yXhaqLIh8TERCa8+yFt2vmTmFj021MSJCYm8vobb/J23/4yph4az6YvtWDy9Fkynh5w8XoiVxMTqV6jJl+v/L7Ix/TSzWRuazM83oO/fZR1ZuVgLq3OwOWrytLd+Rzir97lhsF5AZFCuiWZ3gwZiPwCmDXvY17p2h1UsD0lAcCgYSMYNXaCjKmHxrN9h0588vkyGU8PMKHctMavQUM2bN2lijFNs6mgYSUuyk4GmzLIee3YT/PQDe+FZwDMmD2fLq90BYp+e0oCgP6DhzJ81FgZUw+Np3/7Diz49HMZTw8wOC/05Fu/Ab9u3Cpjmk/FNsqieJEoF86YSpQ9O54SZc+RKBeMRFk8EhLlwhlTibJnx1Oi7DkS5YKRKItHQqJcOGMqUfbseEqUPUeiXDASZfFISJQLZ0wlyp4dT4my50iUC0aiLB4JgGlBc+nY+WX54/TgmPYZMIghw0fJmHpoPNu0C2D+osUynh7ginKdej78smGLjGk+SZTFIwEQNHdBsbiiTnEB2U+JKurtKe4AOnTqwseLl8p4eoAryg0bNWbd5u0ypvkkURaPhMEMV+9ouXAt0eO30vy7Mpjh0s1kLt9KkTH10HgmXL3N9SS9jKeH6M0Qd/kmt++ly5jmk0RZPBJaE6TZlXPLc72JiCjQmGY4nBfTUcH2FHdak/MaBzYZT08yAalWGdP8kigLIYQQKiFRFkIIIVRCoiyEEEKohERZCCGEUAmJshBCCKESEmUhhBBCJSTKQgghhEpIlIUQQgiVkCgLIYQQKiFRFkVCb1YU9XYUV3qzcm1hg0XG0dPjKuPpoXG0yHO0ICTKokikOZRLROpVsC3FjkW5tKYJ5bKQGXbkusKeYIZ0IN0mz8uHHcc0h/LczOSAVIsKtq0YkCiLR8sKDm7x+1dLeW/iFmLtkFbU21ScOIDkSBZP7IafXwN867dhyCcHOOcAs1UF21dM6a2AKZrPhy3k019Oc12elwUbRwsYSWfPgr50aNQQX78G+DZsQrdFIZzTgU3uFPWXJMri0TGDHRvxu4JoU+p5fDp/ywkgvai3q7iwgyP5CAv7dab+q++wcNtWtq2eQtcWgXSauYM4GxhlNvLgLABJ7JrWjQqP+dB3WSRX5Xn5wPRmSLXfY+dHPaj3v/+h+cilfLttJVynCwAAFABJREFUK6snNKds+UrUGbKG8OsWbPai31Y1kyiLR0JrB9L+YPuSAVQv979oNE/Q6O21RMuLX75lADGrB9Gu1au8t02LA4AMQj/pSZ323ZgRpgdH0W9ncaJ1ANf3M2N4G577pwaNxo/ha2K4Ls/LB2awQtqtLfSvrKH261+y67Zyj2pb6h9smdmWUk93Y9qua2iRVYg/I1EWhc8Otnux/DKpDaWrNqP3h/MZ3yGAul2/kyjnlwUcpniW9PKmYZepbL4JOCAV0Ed8Rjc/X9pNPsgNecHLN70NbBe3MLFLRf7ZcCDz5kykddVX6LMsUqJckPE0g153g8P7Qwi/YkZrhVQzGABOfkqT5xvRfWkkl+yyovNnJMqi8FkgPfUuUUd283NoLMm3o/lmwOtU77JKopxfViDjKLM6Nqf928s5lAEWG+gdYLu8geFNAmk24jfOAxlFva3FhQXSky+za+c2Np5Kwpawlu51X+eNz8Mlyg8xphbAChhMzvuoAwk/9KD6//gw8MdYrjsgQw5MzJNEWTwSWrOy/AqA9ggf9+pKjZclyvlmB25sZWSLl2g69CeOW50HdjmA6zsY59+COr1Xc9wCJpmF5JvWogQEgLgVdKjejV5fHJMoe4ge4Op2+jb9H/7dZDLrzhkxIUe3/xmJsigUrvM97zs/0QEkH2HRmxLlB+IAEn6ldyMfag/5iRMWtyhf2864dn549fiSMKMchV0gduDsCjrWkCh7hBkMDuDKDt7pXAlNldeZufMaaVZIlzeNf0qiLDzLopyX6HCyAxlWZRlLZ0KiXFA24N4BPgzwJ2DwD0RkmylvZ2zbQBoOWUesHYyyNPjgJMqeY1aOdeDqDt7pVJF/NR3C3G3X0NvAbAOtUQXbqGISZeExBjvoLxzgswldaNGqDS1ataHdkDn8EGPH6joNQqJcMFYg4xhzOtelefdP+P0eWO3KQTQZZ3+gv08Lmo/cIvuUC0qi7BlmZR/y3YgVjGz7HP/dZAgLQ1IwAya7BDk/JMrCY/RW0N25xP4ta/ji65V88fVKlgWHEH7DQYZrSVWiXDBmsNpS+HlcY156eRw/XlB2g6YDifumEdCyIT2WxpHmPMCmyLe3uJEoPzyz8sY8KWI5fRv/m4pdPuTL8LtkACabCravmJAoC88xgs4KZrJ/GN2XrCTKBWYA7h1eRNfGzeg0P4I7ALZzLOnbnCpd3ufn6zYc8uJXMBLlh6a3QEbKMeZ1qkKFdhP55RL3fZjl0rp/SaIsHi0HkHSQua/4U67NV5yUF79801rAZk0i9IuRNPOrTv3AQDo096NJp4ksClMuypAq+5MLxg6cWUqLcgG8vOgw1+R5+WDMkGqFq7+NwOtxDZoyDWkV2JmOHQJp6x9Iu4BA2gX0Y/72i1y1Qro8T/MkURaPlgXSU5M5GXqQ9fsuccMiy60PxA4WwzVCtqzm08+WsnDJajZFJHIX5QAvbVFvX3FlgfSUi+zYHMbvsSkky/PygenNcOvcIX7+dS3LV6zg0yVLWfjZUj5e7LKGrTHJ3LbIm8c/I1EWj5zWrNzhCMAgB348GCPobG7n1jqPcE81SZAfltaqjKUV0MvzskAMjvuXrN0/rM5zlOW5mjeJshBCCKESEmUhhBBCJSTKQgghhEpIlIUQQgiVkCgLIYQQKiFRFkIIIVRCoiyEEEKohERZCCGEUAmJshBCCKESEmUhhBBCJSTKQgghhEpIlIUQQgiVkCgLIYQQKiFRFkIIIVRCoiyEEEKohERZCCGEUAmJshBCCKESEmUhhBBCJSTKQgghhEpIlIUQQgiVkCgLIYQQKiFRFkIIIVRCoiyEEEKohERZCCGEUAmJshAeksH9H0Yr6Iz5/xlaIxgBO5BmVv7bo9tpBK0ZTM7tSzOBVgVjlxe9Pft4mu2g/4vfz5zL4wCQbi2E8RTCwyTKQjwsZzguhvzM54s+Zc6CT5m74FMWrDlOghEcgMGUv/ilWQ1E7fmNrXvPcSEV0iwe3lYLZKQmcXz/Tr7fFk1COqSaH+xnaI2gt4AlR/Rszt/TI9tphlQH2JOiWbFsCfMWfMq8xZuJuKu8+UnN4//R2+4S+dt6vlyyhPkLPmXuQqdfjhF7x6q8SSrq54sQf0KiLMRD0FvBoTvPrz/MpXeXdgT6d6ZLl8507NKZ+n5t6DtlJSHXjOgceYQkBysJLPYvT6tOC9h+B2wOD28zQGIYc16pxT993+U3LZgfMFQGG6Tok9i3+Qfmf7yYjz5ZyuKVv7A7NpF7dtA/YOTvYwaDFTJuRrFi7CtUa9aegM6dCazXCP+J6zhwy4LBdv+MWW+BtIy9jKtSngrVmtKqc2c6dupMYMfOBE76lUNXzVjtRf+cEeLPSJSFKCC9BdLSrxMS1BGv2s3o/lUMd9xmjtf2vkubUv/A+82v+f1mBhnOkGityvK0+0eaRZmBWrjEV11r06nnl4Qac3yP2TnbNoLOCtZclsq1RkW6c+ZqQZmpuz4yAHQnWNqvEzXbfMS+DMiwQFouy71Gay6zewsY0+9y9ug3DB47lJdf7kK7tvWp+NS/qNduKOuvgMHyF0vMfzWuVkg13uPYwo6U8x3OqnjnBp1fR+fa1fD/4iiX0sBsy/7/GaxgPLuSjs93ZPyma8obkFzGuKifN0L8GYmyEAWUaoWUyAW8VLk2w3+6SgpZodCbwYiDiI/60q53EGvO6kizO/eRGm+xZ/OPLP5iGYuWfMOqsKsk2pRZsYVLLOvqQ483Z7N0w05WfbmMT5atJjjiOrdtYLSA3gHWtJuEbf+exZ8vY9Hn3/DNjlMkpIPdAWkWE+ePhxESFsnRE1H8smQZi5auZM3vp7hsBRwX+GnaJN56dQ1nnNG+lRCu/FtLlrHoi29Ysfkw543cP7O0guneH0RsmceUnyOc+9ENhKyZz8T+Y1hxUvndH2a2nGpzkHRlJ2NffIpOn8ZwLR3MDmW8Q+f6Uarlh2yMz8CIW/yds+u7uyZRtdEHLI81ZQtyqgRZFBMSZSEKwgypFiMH5/tTun0QwfFpmMkxQzSDMf46cRlmbjmUfbfWtOuELn6DWj718WsVQGD7RtRt1Z93VkVzLQ0cXOabN5rQytePZgFv0iYggLY+5ajWajALQ26iBSzpV9m3eBAtG1TH1z+AQP8GeNVvR7/PI7iaAZDMhvcDaVzVm4C3x9MjIAD/Rt54+7Zl3LoEkuxaIg+FsHbNKe5i5NaZYOYN8MenYQD+7QMIbP0iPnWr02nuNk4m3b9fO80B166fZf03i/hl+xZ2HbzMXWf8LLaHH9c0q41bkZ/R4TlvRqy9wQ2zcpBWqtnG+R8G88ITbzH/xB3uuu9bNivhjVnZlRf7jGLCvBUs//prPlv6Nd8duUOSRTlITMIs1E6iLEQB6C1gMJ/h44DylO+1jEPXbdjI5XttStT0JjDg4PamCTSo602PpTEkKnM4Qt8L5D/lujBx8y0yuMV3bzfhmf94M/D7c2gBrv5M75rPUOvtlYQDN4PH89Lz1ek4P9L5M+4R+vFr1KzWlTHBKVgwsGtuD174r1K0nbKbywD6o8zpWInnGr9DcJIyOzYBpMWyfHBzKvuNY7fNNa80cGT1eDr1e5+fz0OGNcebDYuy/J56Yy9TRr9F0/a9WbhwEwcSzKSTY5Zszv2o9Jwf7jPeNIuFm6Hzaf58R6btTSTROYZpFhspu6ZQtWwPph+8SZJDWdLXmZR/M9WWyOpeZalRqTrezTvR2t+ftv7++A96l+VHrpBignRPHzgnhIdJlIUoAIMV9KZ9vFu1HM1GbOJkClj+7KAsCxhNF1ncvQotRqwiLEmJkc4O3AjmzXrVaDL2N+K4x5qedWnaYT5bbjiLZT3LJ928eenVTwi9m8hXPRvgE/gRv2erXSwLugXg9/q3nLFZ2T21Ey/V7sPnsc4vO26yYbI/Teq9wTfxzmVpG6A/w4qRr1LOewTfnkohx25sMiw59itbIM2YyrWz+9iwZT9rtm/li5VTmdiwNs06fsSqeD0ZFucStgW0ej0n937HsuWr+DI3K39g5cGrJKWDwaz8/HSTiau/jKDMs2149/c73MmMsp3kHR/iVbYxQ9Zd5oYtK7J6M6RaY1k9ciBd+i4nJCnrd7iwoj2Nu03nu1PpmHKuZgihMhJlIQpAifIxZjQoR83+a4i47cCaS5RdB3U5AGviZgbXfYbWYzYSqQOLHXQWsGYcZ1bbCrTps4pwh441Pevwcq8v2JMMVgdYucHq3g3p/NoSDh8PZWy72lRoM5LZ333Pt6u+56tV37Nm1Vx6+f2bCi+OYUMa7J3ZkU6Nh/L9FWVftQ0duxb05OV6vfgmIWtfsdFhJj5sOf3qV8W7ei+mfP89K79byy97o7hgBFOOI7PTHWauHdvEnEFvM36fPvNgs5RL6xhS1YvSb2/gilVZSk61w73kGwTPbEuLlq1onps2Hem46BA3UpUlap0F0s1mrm+fRK1ygUzemz3Kd3dNoWrZDryz8xq37VkzZRejc6wznNutt4H97EraVXiJN5cd45JF2S9f1M8fIfIiURaiAJTl62SCR/vyZJcF7Lpsvn/52gxm/Q327d7M1qhE7t7cyXAfJcoRriibwWI/x6cdvOjQexVHnVHu3HMJu5NcUb7Gt283oNNrSzhyPIx3OlXjf8vVokHLljRv0ZJmzVvStHkbWrfvRP/Zv3DCZGbntI50bDSE1ZddUdayc/7rdM4RZa1JudjGnfO7mftmO1q2bEmjupV5vkI1Bq5J4IbZOYM1gc4KNvNVdswfzYt1F3HYeQCV3gp2SwrHvhpJtcpj+TnF+abFiPKmIx/L1wbXhVJc+5QjFtOhbBPe++02t6xZy9fXN03A6+k+zDuRmH2fskn5tyxknS+tNSnbZrqznRH1nqfj+zs4aVD2LRf180eIvEiUhSgI54FFV7aOpXqdrswL0WJEmYW5lntNQPJvY6j0n3/Rcs4Rrt6JZXqrMrQaE8xxvTKj09oA7e+MerE8jQb9RJRDy5oeeUS522LCLp5mSjsfmgxcy1ln0GxkP+0Jktj4YT6j7LbP13Waljn5JJ+8XpGq1Xuz4g+yLrhhA7TxrB/fhSd95nAAMNpAZwEbBqJ+fBefsm+z4ooSQ4MFtAYDp0J+5tvv17IyN2vWsebI9azlazOkWR3cPrOanl5P8vrXl7hpBKMdUm1wYkk7yngN5utTWvRkXaxEbwb9vT/Yv303h+IMJJmUn6e3gvXqr7xeuT0Dl0VwyQ7Ghz2PWohCJFEWooD0VkhLPMi8jl406DGX5VG6zMtXAtw5v5UJTUpRtsVUfjmfhg0rR+d3oH773nx80IDruKqk4HepVb4x3RZHcYc7fNs9jyi/vIA9VgdHZnemXf03mHdIl/kzzIarhG3fyOZDV7iLni2T8xFlC6Sn3SP6cCjrQy+TnLnldo59O5DGZdvy8WklvHoTykw5/Tb7PulNhQoteH/vTfTO/8Nx7Rgre7Wh1is/EW5WgqwsX19n3bTmNGnSjMa5ae5PwIKwrOVrk7LkrDNc5uehdaj91jccdG2YNp6ZXStS/b1fOZXkwOJQ3lA4gHQbGG7sYURtDTVHbeG0285x/fbh+NYdyIzfr6PNObsWQmUkykIUlFm5upU17lcGvFYPb/+JLFi+nnXB6/kpeAVDWtehju+rzNt7ndsOyLCB48pu3nvlBap0msDs1evZEPwJA1o0w6fn5+y6bAcusySgEm26LMy8opeNK3zdzZtWbaexKQW4uoOpr9SnTsBQZv28nuDg9SyZ2I3GFSrzatBuLmBky8QWtKz1NssvKucu27nH1pkdaVP5Zb6IB5vzQC+bLo4N096kduMeTFi9nrXB6wle9THjunWg8eDlHNRmPyUqzQGJlw8w+00fqrTtzaRl6/k1OJjPgybTv8MgVsU6jzY3Z41RGsoS9p/JeSpZmsXO3aPL6VCvAR0mr2DVuvV8N7E7NV8axeJjd7nrgDRzGqfDdrA2NJ4rOjtGB0T/PJzWb/dm+udrCQ5ezw/r17NkymuMXr6NqDvOXQZF/bwR4k9IlIV4SHo72LnMD4N70KpxExo2UjTptZRd12xY3WdnDuD677w/sDONGjWhYaOmdJ61lRPOE32N9ltsmjqYD4I2En4PTDYw2ZPYOncE70/+iYPJynK18dLvfDTS3/kzmvBi07eZtPoMiYDdriNk+STeG/Ep228qFzQx21M58lMQHwyaw+Zrys/VmZzL5+k32PvVSBo3cm17C7qPWc1R0/0HeulMyuw0OSWWzz7oTePGTWjYuA0B09ay4x6YcrthhDnrgiJ5uW9MLcrFQi5tm0bnts7f8aXhrIjJQIsyq9abbrF+cjfavPcTETesWOzKlczOrB1Gt7ZZj8OoNfFcMStBVvPNN4TQmSTKQniE3qwEwezGAqTlPMfXhHJVLOfXXd+b6j4bRTmK2OC8rKbW7XOp5qwDmIxuP8MCGB1Z+1hTHcrX09x+Rqo9++cyt8eszIAtObY/55HN2X5fixJn9//H4zfPMIHBnn2bXJcqdX091aF83uD2O6XalfF1/T9Gu9vBakKonERZCA/Jzwwwr+/N9vV8fu5P/738/twcX8vPtufrdyiscc1jux/2dxFCLSTKQgghhEpIlIUQQgiVkCgLIYQQKiFRFkIIIVRCoiyEEEKohERZCCGEUAmJshBCCKESEmUhhBBCJSTKQgghhEpIlIUQQgiVkCgLIYQQKiFRFkIIIVRCoiyEEEKohERZCCGEUAmJshBCCKESEmUhhBBCJSTKQgghhEpIlIUQQgiVkCgLIYQQKiFRFkIIIVRCoiyEEEKohERZCCGEUIn/D3iJbeg0xUcUAAAAAElFTkSuQmCC" /><br /></span><span lang=""></span></p>
<p class="ImageCaption"><span lang="">Null and alternative distribution,
assuming d = 0.5, alpha = 0.05, and N = 15 per group.</span></p>
<p class="MsoBodyText"><span lang="">You might seen such graphs before. The
only thing I have done is to transform the <i style="mso-bidi-font-style: normal;">t</i>-value
distribution that is commonly used in these graphs, and calculated the
distribution for Cohen’s d. This is a straightforward transformation, but
instead of presenting the critical <i style="mso-bidi-font-style: normal;">t</i>-value
the figure provides the critical <i style="mso-bidi-font-style: normal;">d</i>-value.
For a two-sided independent <i style="mso-bidi-font-style: normal;">t</i>-test,
this is calculated as:</span></p>
<p class="SourceCode"><span style="font-family: "courier";"><span class="KeywordTok"><span lang="">qt</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">(</span></span><span class="DecValTok"><span lang="">1</span></span><span class="OperatorTok"><span lang="">-</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">(a </span></span><span class="OperatorTok"><span lang="">/</span></span><span class="StringTok"><span lang=""> </span></span><span class="DecValTok"><span lang="">2</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">),
(n1 </span></span><span class="OperatorTok"><span lang="">+</span></span><span class="StringTok"><span lang=""> </span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">n2) </span></span><span class="OperatorTok"><span lang="">-</span></span><span class="StringTok"><span lang=""> </span></span><span class="DecValTok"><span lang="">2</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">)
</span></span><span class="OperatorTok"><span lang="">*</span></span><span class="StringTok"><span lang=""> </span></span><span class="KeywordTok"><span lang="">sqrt</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">(</span></span><span class="DecValTok"><span lang="">1</span></span><span class="OperatorTok"><span lang="">/</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">n1 </span></span><span class="OperatorTok"><span lang="">+</span></span><span class="StringTok"><span lang=""> </span></span><span class="DecValTok"><span lang="">1</span></span><span class="OperatorTok"><span lang="">/</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">n2)</span></span></span><span lang=""></span></p>
<p class="FirstParagraph"><span lang="">where ‘a’ is the alpha level (e.g.,
0.05) and N is the sample size in each independent group. For the example
above, where alpha is 0.05 and n = 15:</span></p>
<p class="SourceCode"><span style="font-family: "courier";"><span class="KeywordTok"><span lang="">qt</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">(</span></span><span class="DecValTok"><span lang="">1</span></span><span class="OperatorTok"><span lang="">-</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">(</span></span><span class="FloatTok"><span lang="">0.05</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;"> </span></span><span class="OperatorTok"><span lang="">/</span></span><span class="StringTok"><span lang=""> </span></span><span class="DecValTok"><span lang="">2</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">),
(</span></span><span class="DecValTok"><span lang="">15</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;"> </span></span><span class="OperatorTok"><span lang="">*</span></span><span class="StringTok"><span lang=""> </span></span><span class="DecValTok"><span lang="">2</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">)
</span></span><span class="OperatorTok"><span lang="">-</span></span><span class="StringTok"><span lang=""> </span></span><span class="DecValTok"><span lang="">2</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">) </span></span><span class="OperatorTok"><span lang="">*</span></span><span class="StringTok"><span lang=""> </span></span><span class="KeywordTok"><span lang="">sqrt</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">(</span></span><span class="DecValTok"><span lang="">1</span></span><span class="OperatorTok"><span lang="">/</span></span><span class="DecValTok"><span lang="">15</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;"> </span></span><span class="OperatorTok"><span lang="">+</span></span><span class="StringTok"><span lang=""> </span></span><span class="DecValTok"><span lang="">1</span></span><span class="OperatorTok"><span lang="">/</span></span><span class="DecValTok"><span lang="">15</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">)</span></span><span lang=""></span></span></p><span style="font-family: "courier";">
</span><p class="SourceCode"><span style="font-family: "courier";"><span class="VerbatimChar"><span lang="" style="color: black; mso-color-alt: windowtext;">## [1] 0.7479725</span></span></span><span lang=""></span></p>
<p class="FirstParagraph"><span lang="">The critical <i style="mso-bidi-font-style: normal;">t</i>-value (2.0484071) is also provided in commonly used power
analysis software such as G*Power. We can compute the critical Cohen’s d from
the <i style="mso-bidi-font-style: normal;">t</i>-value and sample size using </span><span class="math inline"><span class="MathJax" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML"><mi>d</mi><mo>=</mo><mi>t</mi><mrow class="MJX-TeXAtom-ORD"><msqrt><mn>1</mn><mrow class="MJX-TeXAtom-ORD"><mo>/</mo></mrow><msub><mi>n</mi><mn>1</mn></msub><mo>+</mo><mn>1</mn><mrow class="MJX-TeXAtom-ORD"><mo>/</mo></mrow><msub><mi>n</mi><mn>2</mn></msub></msqrt></mrow></math>" id="MathJax-Element-1-Frame" role="presentation" style="position: relative;" tabindex="0"><nobr aria-hidden="true"><span class="math" id="MathJax-Span-1" style="display: inline-block; width: 9.229em;"><span style="display: inline-block; font-size: 120%; height: 0px; position: relative; width: 7.679em;"><span style="clip: rect(1.498em, 1007.68em, 3.087em, -1000em); left: 0em; position: absolute; top: -2.56em;"><span class="mrow" id="MathJax-Span-2"><span class="mi" id="MathJax-Span-3" style="font-family: STIXGeneral; font-style: italic;">d<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.027em;"></span></span><span class="mo" id="MathJax-Span-4" style="font-family: STIXGeneral; padding-left: 0.313em;">=</span><span class="mi" id="MathJax-Span-5" style="font-family: STIXGeneral; font-style: italic; padding-left: 0.313em;">t<span style="display: inline-block; height: 1px; overflow: hidden; width: 0.018em;"></span></span><span class="texatom" id="MathJax-Span-6"><span class="mrow" id="MathJax-Span-7"><span class="msqrt" id="MathJax-Span-8"><span style="display: inline-block; height: 0px; position: relative; width: 5.551em;"><span style="clip: rect(3.134em, 1004.6em, 4.317em, -1000em); left: 0.928em; position: absolute; top: -3.988em;"><span class="mrow" id="MathJax-Span-9"><span class="mn" id="MathJax-Span-10" style="font-family: STIXGeneral;">1</span><span class="texatom" id="MathJax-Span-11"><span class="mrow" id="MathJax-Span-12"><span class="mo" id="MathJax-Span-13" style="font-family: STIXGeneral;">/</span></span></span><span class="msubsup" id="MathJax-Span-14"><span style="display: inline-block; height: 0px; position: relative; width: 0.929em;"><span style="clip: rect(3.369em, 1000.47em, 4.176em, -1000em); left: 0em; position: absolute; top: -3.988em;"><span class="mi" id="MathJax-Span-15" style="font-family: STIXGeneral; font-style: italic;">n</span><span style="display: inline-block; height: 3.988em; width: 0px;"></span></span><span style="left: 0.5em; position: absolute; top: -3.838em;"><span class="mn" id="MathJax-Span-16" style="font-family: STIXGeneral; font-size: 70.7%;">1</span><span style="display: inline-block; height: 3.988em; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-17" style="font-family: STIXGeneral; padding-left: 0.25em;">+</span><span class="mn" id="MathJax-Span-18" style="font-family: STIXGeneral; padding-left: 0.25em;">1</span><span class="texatom" id="MathJax-Span-19"><span class="mrow" id="MathJax-Span-20"><span class="mo" id="MathJax-Span-21" style="font-family: STIXGeneral;">/</span></span></span><span class="msubsup" id="MathJax-Span-22"><span style="display: inline-block; 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height: 3.988em; width: 0px;"></span></span><span style="font-family: STIXGeneral; left: 2.464em; position: absolute; top: -3.988em;">‾<span style="display: inline-block; height: 3.988em; width: 0px;"></span></span><span style="font-family: STIXGeneral; left: 2.879em; position: absolute; top: -3.988em;">‾<span style="display: inline-block; height: 3.988em; width: 0px;"></span></span><span style="font-family: STIXGeneral; left: 3.293em; position: absolute; top: -3.988em;">‾<span style="display: inline-block; height: 3.988em; width: 0px;"></span></span><span style="font-family: STIXGeneral; left: 3.708em; position: absolute; top: -3.988em;">‾<span style="display: inline-block; height: 3.988em; width: 0px;"></span></span></span><span style="display: inline-block; height: 3.988em; width: 0px;"></span></span><span style="clip: rect(2.837em, 1000.96em, 4.426em, -1000em); left: 0em; position: absolute; top: -3.898em;"><span style="font-family: STIXGeneral;">√</span><span style="display: inline-block; height: 3.988em; width: 0px;"></span></span></span></span></span></span></span><span style="display: inline-block; height: 2.56em; width: 0px;"></span></span></span><span style="border-left: 0px solid; display: inline-block; height: 1.621em; overflow: hidden; vertical-align: -0.49em; width: 0px;"></span></span></nobr></span></span><span class="math inline"></span><span lang="">.</span></p>
<p class="CaptionedFigure"><span lang="" style="mso-no-proof: yes;"><br /></span></p><p class="CaptionedFigure"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://1.bp.blogspot.com/-caeI3of_VLM/Xyp0sBUFvOI/AAAAAAAAuUs/mkS_OfYpcxISOyQiKZHJ1zhJ9lCHVNIZACLcBGAsYHQ/s913/gpowcrit2.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="913" data-original-width="757" height="640" src="https://1.bp.blogspot.com/-caeI3of_VLM/Xyp0sBUFvOI/AAAAAAAAuUs/mkS_OfYpcxISOyQiKZHJ1zhJ9lCHVNIZACLcBGAsYHQ/s640/gpowcrit2.png" /></a></div><br /><span lang="" style="mso-no-proof: yes;"><br /></span><span lang=""></span><p></p>
<p class="ImageCaption"><i><span lang="">The critical t-value is provided by
G*Power software.</span></i></p>
<p class="MsoBodyText"><span lang="">When you will test an association between
variables with a correlation, G*Power will directly provide you with the
critical effect size. When you compute a correlation based on a two-sided test,
your alpha level is 0.05, and you have 30 observations, only effects larger
than r = 0.361 will be statistically significant. In other words, the effect
needs to be quite large to even have the mathematical possibility of becoming
statistically significant.</span></p>
<p class="CaptionedFigure"><span lang="" style="mso-no-proof: yes;"></span><br /><a href="https://1.bp.blogspot.com/-P9h4DPvE-W4/Xyp0sPlyP0I/AAAAAAAAuUo/EalX67JD0pwiIEPbPcSTh1NiaHiKV7xVACLcBGAsYHQ/s913/gpowcrit.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="913" data-original-width="757" height="640" src="https://1.bp.blogspot.com/-P9h4DPvE-W4/Xyp0sPlyP0I/AAAAAAAAuUo/EalX67JD0pwiIEPbPcSTh1NiaHiKV7xVACLcBGAsYHQ/s640/gpowcrit.png" /></a><span lang="" style="mso-no-proof: yes;"></span><span lang=""></span></p>
<p class="ImageCaption"><span lang="">The critical r is provided by G*Power
software.</span></p>
<p class="MsoBodyText"><span lang="">The critical effect size gives you
information about the smallest effect size that, if observed, would by
statistically significant. If you observe a smaller effect size, the <i style="mso-bidi-font-style: normal;">p</i>-value will be larger than your
significance threshold. You always have some probability of observing effects
larger than the critical effect size. After all, even if the null hypothesis is
true, 5% of your tests will yield a significant effect. But what you should ask
yourself is whether the effect sizes that could be statistically significant
are realistically what you would expect to find. If this is not the case, it
should be clear that there is little (if any) use in performing a significance
test. Mathematically, when the critical effect size is larger than effects you
expect, your statistical power will be less than 50%. If you perform a
statistical test with less than 50% power, your single study is not very
informative. Reporting the critical effect size in a feasibility justification
should make you reflect on whether a hypothesis test will yield an informative
answer to your research question.</span></p>
<h3><a name="Xf9924d1bc9a5e419c22842c802bc3881a132cbd"><span lang="">Compute
the width of the confidence interval around the effect size</span></a><span lang=""></span></h3>
<p class="FirstParagraph"><span lang="">The second statistic to report
alongside a feasibility justification is the width of the 95% confidence
interval around the effect size. 95% confidence intervals will capture the true
population parameter 95% of the time in repeated identical experiments. The
more uncertain we are about the true effect size, the wider a confidence
interval will be. Cumming (2013) calls the difference
between the observed effect size and its upper 95% confidence interval (or the
lower 95% confidence interval) the margin of error (MOE).</span></p>
<p class="SourceCode"><span style="font-family: "courier";"><span class="CommentTok"><span lang=""># Compute the
effect size d and 95% CI</span></span><span lang="" style="color: black; mso-color-alt: windowtext;"><br />
<span class="NormalTok">res <-</span></span><span class="StringTok"><span lang=""> </span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">MOTE</span></span><span class="OperatorTok"><span lang="">::</span></span><span class="KeywordTok"><span lang="">d.ind.t</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">(</span></span><span class="DataTypeTok"><span lang="">m1 =</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;"> </span></span><span class="DecValTok"><span lang="">0</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">, </span></span><span class="DataTypeTok"><span lang="">m2 =</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;"> </span></span><span class="DecValTok"><span lang="">0</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">, </span></span><span class="DataTypeTok"><span lang="">sd1 =</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;"> </span></span><span class="DecValTok"><span lang="">1</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">, </span></span><span class="DataTypeTok"><span lang="">sd2 =</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;"> </span></span><span class="DecValTok"><span lang="">1</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">, </span></span><span class="DataTypeTok"><span lang="">n1 =</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;"> </span></span><span class="DecValTok"><span lang="">15</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">, </span></span><span class="DataTypeTok"><span lang="">n2 =</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;"> </span></span><span class="DecValTok"><span lang="">15</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">, </span></span><span class="DataTypeTok"><span lang="">a =</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;"> </span></span><span class="FloatTok"><span lang="">.05</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">)</span></span></span><span lang=""></span></p>
<p class="SourceCode"><span class="VerbatimChar"><span lang="" style="color: black; mso-color-alt: windowtext;"></span></span><span lang="" style="color: black; mso-color-alt: windowtext;"><span class="VerbatimChar"></span></span><span lang=""></span></p>
<p class="SourceCode"><span style="font-family: "courier";"><span class="CommentTok"><span lang=""># Print the result</span></span><span lang="" style="color: black; mso-color-alt: windowtext;"><br />
<span class="NormalTok">res</span></span><span class="OperatorTok"><span lang="">$</span></span><span class="NormalTok"><span lang="" style="color: black; mso-color-alt: windowtext;">estimate</span></span><span lang=""></span></span></p><span style="font-family: "courier";">
</span><p class="SourceCode"><span style="font-family: "courier";"><span class="VerbatimChar"><span lang="" style="color: black; mso-color-alt: windowtext;">## [1] "$d_s$ = 0.00, 95\\% CI [-0.72,
0.72]"</span></span></span><span lang=""></span></p><span class="VerbatimChar"><span lang="" style="color: black; mso-color-alt: windowtext;"></span></span><span lang=""></span>
<p class="FirstParagraph"><span lang="">If we compute the 95% CI for an effect
size of 0, we see that with 15 observations in each condition of an independent
<i style="mso-bidi-font-style: normal;">t</i>-test the 95% CI ranges from -0.72
to 0.72. The MOE is half the width of the 95% CI, 0.72. This clearly shows we
have a very imprecise estimate. A Bayesian estimator who uses an uninformative
prior would compute a credible interval with the same upper and lower bound, and might conclude they personally believe there is a
95% chance the true effect size lies in this interval. A frequentist would
reason more hypothetically: If the observed effect size in the data I plan to
collect is 0, I could only reject effects more extreme than d = 0.72 in an
equivalence test with a 5% alpha level (even though if such a test would be
performed, power might be low, depending on the true effect size). Regardless
of the statistical philosophy you plan to rely on when analyzing the data, our
evaluation of what we can conclude based on the width of our interval tells us
we will not learn a lot. Effect sizes in the range of d = 0.7 are findings such
as “People become aggressive when they are provoked”, “People prefer their own
group to other groups”, and “Romantic partners resemble one another in physical
attractiveness” (Richard et al., 2003). The width of the confidence interval tells
you that you can only reject the presence of effects that are so large, if they
existed, you would probably already have noticed them. It might still be
important to establish these large effects in a well-controlled experiment. But
since most effect sizes in we should realistically expect are much smaller, we
do not learn something we didn’t already know from the data that plan to
collect. Even without data, we would exclude effects larger than d = 0.7 in most
research lines.</span></p>
<p class="MsoBodyText"><span lang="">We see this the MOE is almost, but not
exactly, the same as the critical effect size d we observed above (d =
0.7479725). The reason for this is that the 95% confidence interval is
calculated based on the <i style="mso-bidi-font-style: normal;">t</i>-distribution.
If the true effect size is not zero, the confidence interval is calculated
based on the non-central <i style="mso-bidi-font-style: normal;">t</i>-distribution,
and the 95% CI is asymmetric. The figure below vizualizes three <i style="mso-bidi-font-style: normal;">t</i>-distributions, one symmetric at 0, and
two asymmetric distributions with a noncentrality parameter of 2 and 3. The
asymmetry is most clearly visible in very small samples (the distribution in
the plot have 5 degrees of freedom) but remain noticeable when calculating
confidence intervals and statistical power. For example, for a true effect size
of d = 0.5 the 95% CI is </span><span lang="">[-0.23, 1.22]. The MOE based
on the lower bound is 0.7317584 and based on the upper bound is 0.7231479. If
we compute the 95% CI around the critical effect size (d = 0.7479725) we see
the 95% CI ranges from exactly 0.00 to 1.48. If the 95% CI excludes zero, the
test is statistically significant. In this case the lowerbound of the
confidence interval exactly touches 0, which means we would observe a <i style="mso-bidi-font-style: normal;">p</i> = 0.05 if we exactly observed the
critical effect size.</span></p>
<p class="CaptionedFigure"><span lang="" style="mso-no-proof: yes;"><img alt="" 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" /><br /></span><span lang=""></span></p>
<p class="ImageCaption"><i><span lang="">Central (black) and 2 non-central (red
and blue) t-distributions.</span></i></p>
<p class="MsoBodyText"><span lang="">Where computing the critical effect size
can make it clear that a <i style="mso-bidi-font-style: normal;">p</i>-value is
of little interest, computing the 95% CI around the effect size can make it
clear that the effect size estimate is of little value. It will often be so
uncertain, and the range of effect sizes you will not be able to reject if
there is no effect is so large, the effect size estimate is not very useful.
This is also the reason why performing a pilot study to estimate an effect size
for an a-priori power analysis is not a sensible strategy (Albers & Lakens, 2018; Leon et al, 2011). Your effect size estimate will be so uncertain, it is not a
good guide in an a-priori power analysis.</span></p>
<p class="MsoBodyText"><span lang="">However, it is possible that the sample
size is large enough to exclude some effect sizes that are still a-priori
plausible. For example, with 50 observations in each independent group, you
have 82% power for an equivalence test with bounds of -0.6 and 0.6. If the
literature includes claims of effect size estimates larger than 0.6, and if
effect larger than 0.6 can be rejected based on your data, this might be
sufficient to tentatively start to question claims in the literature, and the
data you collect might fulfill that very specific goal.</span></p>
<h3><a name="sensitivitypower"><span lang="">Plot a sensitivity power analysis</span></a><span lang=""></span></h3>
<p class="FirstParagraph"><span lang="">In a <b style="mso-bidi-font-weight: normal;">sensitivity power analysis</b> the sample size and the alpha level are
fixed, and you compute the effect size you have the desired statistical power
to detect. For example, in the Figure below the sample size in each group
is set to 15, the alpha level is 0.05, and the desired power is set to 90%. The
sensitivity power analysis shows we have 90% power to detect an effect of d =
1.23.</span></p>
<p class="CaptionedFigure"></p><div class="separator" style="clear: both; text-align: center;"><a href="https://1.bp.blogspot.com/-b0FrOfEmnzw/Xyp03zO06dI/AAAAAAAAuUw/76kDpva8muomq6XtfjAcbaV1WVo8oK-pACLcBGAsYHQ/s913/sensitivity0.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="913" data-original-width="757" height="640" src="https://1.bp.blogspot.com/-b0FrOfEmnzw/Xyp03zO06dI/AAAAAAAAuUw/76kDpva8muomq6XtfjAcbaV1WVo8oK-pACLcBGAsYHQ/s640/sensitivity0.png" /></a></div><span lang="" style="mso-no-proof: yes;"><br /></span><span lang=""></span><p></p>
<p class="ImageCaption"><i><span lang="">Sensitivity power analysis in G*Power
software.</span></i></p>
<p class="MsoBodyText"><span lang="">Perhaps you feel a power of 90% is a bit
high, and you would be happy with 80% power. We can plot a sensitivity curve
across all possible levels of statistical power. In the figure below we
see that if we desire 80% power, the effect size should be d = 1.06. The
smaller the true effect size, the lower the power we have. This plot should
again remind us not to put too much faith in a significance test when are
sample size is small, since for 15 observations in each condition, statistical
power is very low for anything but extremely large effect sizes.</span></p>
<p class="CaptionedFigure"></p><div><i><div class="separator" style="clear: both; text-align: center;"><a href="https://1.bp.blogspot.com/-wdFdI0_olf0/XyqLJhS8DQI/AAAAAAAAuV0/iO_xXgCk35M-oBc1sAAyfb_usriTtR4uACLcBGAsYHQ/s970/sensitivity1.png" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="845" data-original-width="970" src="https://1.bp.blogspot.com/-wdFdI0_olf0/XyqLJhS8DQI/AAAAAAAAuV0/iO_xXgCk35M-oBc1sAAyfb_usriTtR4uACLcBGAsYHQ/s640/sensitivity1.png" width="640" /></a></div><span lang=""><br /></span></i></div><div><i><span lang="">Plot of the effect size against the
desired power when n = 15 per group and alpha = 0.05.</span></i><i>
</i></div><p class="MsoBodyText"><span lang="">If we look at the effect size that we
would have 50% power for, we see it is d = 0.7411272. This is very close to our
critical effect size of d = 0.7479725 (the smallest effect size that, if
observed, would be significant). The difference is due to the non-central <i style="mso-bidi-font-style: normal;">t</i>-distribution.</span></p>
<h3><a name="reporting-a-feasibility-justification."><span lang="">Reporting a
feasibility justification.</span></a><span lang=""></span></h3>
<p class="FirstParagraph"><span lang="">To summarize, I recommend addressing
the following components in a feasibility sample size justification. Addressing
these points explicitly will allow you to evaluate for yourself if collecting
the data will have scientific value. If not, there might be other reasons to
collect the data. For example, at our department, students often collect data
as part of their education. However, if the primary goal of data collection is
educational, the sample size that is collected can be very small. It is often
educational to collect data from a small number of participants to experience
what data collection looks like in practice, but there is often no educational
value in collecting data from more than 10 participants. Despite the small
sample size, we often require students to report statistical analyses as part
of their education, which is fine as long as it is clear the numbers that are
calculated can not meaningfully be interpreted. Te table below
should help to evaluate if the interpretation of statistical tests has any
value, or not.</span></p>
<p class="TableCaption"><i><span lang="">Overview of recommendations when
reporting a sample size justification based on feasibility.</span></i></p>
<table border="0" cellpadding="0" cellspacing="0" class="Table" style="border-collapse: collapse; mso-padding-alt: 0cm 5.4pt 0cm 5.4pt; mso-yfti-tbllook: 2016; width: 100%;" title="Overview of recommendations when reporting a sample size justification based on feasibility.">
<tbody><tr style="mso-yfti-firstrow: yes; mso-yfti-irow: 0;">
<td style="border: medium none; mso-border-bottom-alt: solid windowtext .25pt; padding: 0cm 5.4pt;" valign="bottom">
<p class="Compact"><span lang="">What to address?</span></p>
</td>
<td style="border: medium none; mso-border-bottom-alt: solid windowtext .25pt; padding: 0cm 5.4pt;" valign="bottom">
<p class="Compact"><span lang="">How to address it?</span></p>
</td>
</tr>
<tr style="mso-yfti-irow: 1;">
<td style="padding: 0cm 5.4pt;" valign="top">
<p class="Compact"><span lang="">Will a future meta-analysis be performed?</span></p>
</td>
<td style="padding: 0cm 5.4pt;" valign="top">
<p class="Compact"><span lang="">Consider the plausibility that sufficient
highly similar studies will be performed in the future to, eventually, make a
meta-analysis possible</span></p>
</td>
</tr>
<tr style="mso-yfti-irow: 2;">
<td style="padding: 0cm 5.4pt;" valign="top">
<p class="Compact"><span lang="">Will a decision be made, regardless of the
amount of data that is available?</span></p>
</td>
<td style="padding: 0cm 5.4pt;" valign="top">
<p class="Compact"><span lang="">If it is known that a decision will be
made, with or without data, then any data you collect will reduce error
rates.</span></p>
</td>
</tr>
<tr style="mso-yfti-irow: 3;">
<td style="padding: 0cm 5.4pt;" valign="top">
<p class="Compact"><span lang="">What is the critical effect size?</span></p>
</td>
<td style="padding: 0cm 5.4pt;" valign="top">
<p class="Compact"><span lang="">Report and interpret the critical effect
size, with a focus on whether a hypothesis test would even be significant for
expected effect sizes. If not, indicate you will not interpret the data based
on <i style="mso-bidi-font-style: normal;">p</i>-values.</span></p>
</td>
</tr>
<tr style="mso-yfti-irow: 4;">
<td style="padding: 0cm 5.4pt;" valign="top">
<p class="Compact"><span lang="">What is the width of the confidence
interval?</span></p>
</td>
<td style="padding: 0cm 5.4pt;" valign="top">
<p class="Compact"><span lang="">Report and interpret the width of the
confidence interval. What will an estimate with this much uncertainty be
useful for? If the null hypothesis is true, would rejecting effects outside
of the confidence interval be worthwhile (ignoring you might have low power
to actually test against these values)?</span></p>
</td>
</tr>
<tr style="mso-yfti-irow: 5; mso-yfti-lastrow: yes;">
<td style="padding: 0cm 5.4pt;" valign="top">
<p class="Compact"><span lang="">Which effect sizes would you have decent
power to detect?</span></p>
</td>
<td style="padding: 0cm 5.4pt;" valign="top">
<p class="Compact"><span lang="">Report a sensitivity power analysis, and
report the effect sizes you could detect across a range of desired power
levels (e.g., 80%, 90%, and 95%), or plot a sensitivity curve of effect sizes
against desired power.</span></p>
</td>
</tr>
</tbody></table>
<p class="MsoBodyText"><span lang=""><br /></span></p><p class="MsoBodyText"><span lang=""><br /></span></p><p class="MsoBodyText"><span lang="">If the study is not performed for
educational purposes, but the goal is answer a research question, the
feasibility justification might indicate that there is no value in collecting
the data. If it wasn’t possible to conclude that one should not proceed with
the data collection, there is no use of justifying the sample size. There
should be cases where it is unlikely there will ever be enough data to perform
a meta-analysis (for example because of a lack of general interest in the
topic), the information will not be used to make any decisions, and the
statistical tests do not allow you to test a hypothesis or estimate an effect
size estimate with any useful accuracy. It should be a feasibility
justification - not a feasibility excuse. If there is no good justification to
collect the maximum number of observations that is feasible, performing the
study nevertheless is a waste of participants time, and/or a waste of money if
data collection has associated costs. Collecting data without a good
justification why the planned sample size will yield worthwhile information has
an ethical component. As Button and colleagues Button et al (2013) write:</span></p><i><b>
</b></i><p class="MsoBlockText"><i><b><span lang="">Low power therefore has an ethical
dimension — unreliable research is inefficient and wasteful. This applies to
both human and animal research.</span></b></i></p>
<p class="FirstParagraph"><span lang="">Think carefully if you can defend data
collection based on a feasibility justification. Sometimes data collection is
just not feasible, and we should accept this.</span></p>
Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com3tag:blogger.com,1999:blog-987850932434001559.post-25642416921107691402020-07-28T22:01:00.003+02:002020-07-29T17:30:46.922+02:00The Red Team Challenge (Part 4): The Wildcard Reviewer<div><i>This is a guest blog by <a href="https://twitter.com/lubianat" target="_blank">Tiago Lubiana</a>,<span style="background: white none repeat scroll 0% 0%; color: black;"> Ph.D. Candidate in
Bioinformatics, </span><span style="color: black;">University of São Paulo.</span>
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<![endif]--></div><div><br /></div><div>Read also <a href="http://www.the100.ci/2020/06/29/red-team-part-1/">Part 1</a>, <a href="http://www.the100.ci/2020/06/30/red-team-part-2/">Part 2</a>, and <a href="https://daniellakens.blogspot.com/2020/07/the-red-team-challenge-part-3-is-it.html">Part 3</a> of The Red Team Challenge</div><br />Two remarkable moments as a researcher are publishing your first first-author article and the first time a journal editor asks you to review a paper. <br /><br />Well, at least I imagine so. I haven’t experienced either yet. Yet,for some reason, the author of the <a href="https://daniellakens.blogspot.com/2020/05/red-team-challenge.html">Red Team Challenge</a> accepted me as a (paid) reviewer for their audacious project.<br /><br />I believe I am one of the few scientists to receive money for a peer review before doing any unpaid peer-reviews. I’m also perhaps one of the few to review a paper before having any first-author papers. Quite likely, I am the first one to do both at the same time. <br /><br />I am, nevertheless, a science aficionado. I’ve breathed science for the past 9 years, working in10 different laboratories before joining the Computational Systems Biology Lab at the University of São Paulo, where I am pursuing my PhD. I like this whole thing of understanding more about the world, reading, doing experiments, sharing findings with people. That is my thing (to be fair, that is likely our thing). <br /><br />I also had my crises with the scientific world. A lot of findings in the literature are contradictory. And many others are simply wrong. And they stay wrong, right? It is incredible, but people usually do not update articles even given a need for corrections. The all-powerful, waxy stamp of peer-reviewed is given to a monolithic text-and-figure-and-table PDF, and this pdf is then frozen forever in the hall of fame. And it costs a crazy amount of money to lock this frozen pdf behind paywalls.<br /><br />I have always been very thorough in my evaluation of any work. With time, I discovered that, for some reason, people don’t like to have their works criticized (who would imagine, huh?). That can be attenuated by a lot of training in how to communicate. But even then, people frown upon even constructive criticism. If it is a critic about something that is already published, then it is even worse. So I got quite excited when I saw this call for people to have a carte blanche to criticize a piece of work as much as possible. <br /><br />I got to know the Red Team Challenge via a Whatsapp message sent by Olavo Amaral, who leads the <a href="https://elifesciences.org/articles/41602v1">Brazilian Reproducibility Initiative</a>. Well, it looked cool, it paid a fairly decent amount of money, and the application was simple (it did not require a letter of recommendation or anything like this). So I thought: “Why not? I do not know a thing about psychology, but I can maybe spot a few uncorrected multiple comparisons here and there, and I can definitely look at the code.”<br /><br />I got lucky that the Blue Team (Coles et al.) had a place for random skills (the so-called <a href="http://www.the100.ci/2020/07/01/red-team-part-3/">wildcard category</a>) in their system for selecting reviewers. About a week after applying, I received a message in my mail that stated that I had been chosen as a reviewer. “Great! What do I do now?” <br /><br />I was obviously a bit scared of making a big blunder or at least just making it way below expectations. But there was a thing that tranquilized me: I was hired. This was not an invitation based on expectations or a pre-existing relationship with the ones hiring me. People were actually paying me, and my tasks for the job were crystal clear. <br /><br />If I somehow failed to provide a great review, it would not affect my professional life whatsoever (I guess). I had just the responsibility to do a good job that any person has after signing a contract. <br /><br />I am not a psychologist by training, and so I knew beforehand that the details of the work would be beyond my reach. However, after reading the manuscript, I felt even worse: the manuscript was excellent. Or I mean, at least a lot of care was taken when planning and important experimental details as far as I could tell as an outsider. <br /><br />It is not uncommon for me to cringe upon a few dangling uncorrected p-values here and there, even when reading something slightly out of my expertise. Or to find some evidence of optional stopping. Or pinpoint some statistical tests from which you cannot really tell what the null hypothesis is and what actually is being tested. <br /><br />That did not happen. However, everyone involved knew that I was not a psychologist. I was plucked from the class of miscellaneous reviewers. From the start, I knew that I could contribute the most by reviewing the code. <br /><br />I am a computational biologist, and our peers in the computer sciences usually look down on our code. For example, software engineers called a high profile epidemiological modeling code a “<a href="http://www.the100.ci/2020/06/30/red-team-part-2/">null</a> I would say that lack of computational reproducibility is pervasive throughout science, and not restricted to a discipline or the other. <br /><br />Luckily, I have always been interested in best practices. I might not always follow them (“do what I say not what I do”), mainly because of environmental constraints. Journals don’t require clean code, for example. And I’ve never heard about “proofreadings” of scripts that come alongside papers. <br /><br />It was a pleasant surprise to see that the code from the paper was good, better than most of the code I’ve seen in biology-related scripts. It was filled with comments. The required packages were laid down at the beginning of the script. The environment was cleared in the first line so to avoid dangling global variables.<br /><br />These are all good practices. If journals asked reviewers to check code (which they usually do not), it would come out virtually unscathed. <br /><br />But I was being paid to review it, so I had to find something. There are some things that can improve one’s code and make it much easier to check and validate. One can avoid commenting too much by using clear variable names, and you do not have to lay down the packages used if the code is containerized (with Docker, for example). A bit of refactoring could be done here and there, also, extracting out functions that were repeatedly used across the code. That was mostly what my review focused on, honestly. <br /><br />Although these things are relatively minor, they do make a difference. It is a bit like the difference in prose between a non-writer and an experienced writer. The raw content might be the same, but the effectiveness of communication can vary a lot. And reading code can be already challenging, so it is always good to make it easier for the reader (and the reviewer, by extension). <br /><br />Anyways, I have sent 11 issue reports (below the mean of ~20, but precisely the median of 11 reports/reviewer), and<a href="https://www.blogger.com/#"> Ruben Arslan, the neutral arbiter</a>, considered one of them to be a major issue. Later, Daniël and Nicholas mentioned that the reviews were helpful, so I am led to believe that somehow I contributed to future improvements in this report. Science wins, I guess. <br /><br />One interesting aspect of being hired by the authors is that I did not feel compelled to state whether I thought the work was relevant or novel. The work is obviously important for the authors who hired me. The current peer-review system mixes the evaluation of thoroughness and novelty under the same brand. That might be suboptimal in some cases. A good reviewer for statistics, or code, for example, might not feel that they can tell how much a “contribution is significant or only incremental,” <a href="https://www.wikihow.com/Write-a-Peer-Review-Report">as currently required</a>. If that was a requirement for the Red Team Challenge, I would not have been able to be a part of the Red Team. <br /><br />This mix of functions may be preventing us from getting more efficient reviews. We know that gross mistakes pass peer review. I’d trust a regularly updated preprint, with thorough, open, commissioned peer review, for example. I am sure we can come up with better ways of giving “this-is-good-science” stamps and improve the effectiveness of peer reviews. <br /><br />To sum up, it felt very good to be in a system with the right incentives. Amidst this whole pandemic thing and chaos everywhere, I ended up being part of something really wonderful. Nicholas, Daniël, and all the others involved in the Red Team challenge are providing prime evidence that an alternate system is viable. Maybe one day, we will have<a href="http://www.the100.ci/2020/07/01/red-team-part-3/"> reviewer-for-hire marketplaces</a> and more adequate review incentives. When that day comes, I will be there, be it hiring or being hired.<br />Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com0tag:blogger.com,1999:blog-987850932434001559.post-44356223998848554422020-07-01T09:00:00.006+02:002021-03-14T15:05:59.155+01:00The Red Team Challenge (Part 3): Is it Feasible in Practice?
<p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;"><u><i>By Daniel Lakens & Leo Tiokhin</i></u></span></p><p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;"><u><i><i>Also read <a href="http://www.the100.ci/2020/06/29/red-team-part-1/">Part 1</a> and <a href="http://www.the100.ci/2020/06/30/red-team-part-2/">Part 2</a></i> <i>in this series on our Red Team Challenge</i>.</i></u></span></p><p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;"><u><i><br /></i></u></span></p><p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;"></span></p><!--[if gte mso 9]><xml>
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="header"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="footer"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" Priority="35" SemiHidden="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="envelope address"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="footnote reference"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="line number"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="page number"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="endnote reference"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="endnote text"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="table of authorities"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="macro"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="toa heading"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="List Number"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" Priority="10" QFormat="true" Name="Title"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" Priority="1" SemiHidden="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" Priority="11" QFormat="true" Name="Subtitle"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" Priority="22" QFormat="true" Name="Strong"/>
<w:LsdException Locked="false" Priority="20" QFormat="true" Name="Emphasis"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Acronym"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Address"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
Name="HTML Preformatted"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" Priority="39" Name="Table Grid"/>
<w:LsdException Locked="false" SemiHidden="true" UnhideWhenUsed="true"
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<w:LsdException Locked="false" SemiHidden="true" Name="Placeholder Text"/>
<w:LsdException Locked="false" Priority="1" QFormat="true" Name="No Spacing"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading"/>
<w:LsdException Locked="false" Priority="61" Name="Light List"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1"/>
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<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3"/>
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<w:LsdException Locked="false" Priority="71" Name="Colorful Shading"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 1"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 1"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 1"/>
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<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 1"/>
<w:LsdException Locked="false" SemiHidden="true" Name="Revision"/>
<w:LsdException Locked="false" Priority="34" QFormat="true"
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<w:LsdException Locked="false" Priority="29" QFormat="true" Name="Quote"/>
<w:LsdException Locked="false" Priority="30" QFormat="true"
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<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 1"/>
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<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 1"/>
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<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 1"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 1"/>
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<w:LsdException Locked="false" Priority="61" Name="Light List Accent 2"/>
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<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 2"/>
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<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 2"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 2"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 3"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 3"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 3"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 3"/>
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<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 3"/>
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<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 3"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 3"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 4"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 4"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 4"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 4"/>
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<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 4"/>
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<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 4"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 4"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 5"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 5"/>
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<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 5"/>
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<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 5"/>
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<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 5"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 5"/>
<w:LsdException Locked="false" Priority="60" Name="Light Shading Accent 6"/>
<w:LsdException Locked="false" Priority="61" Name="Light List Accent 6"/>
<w:LsdException Locked="false" Priority="62" Name="Light Grid Accent 6"/>
<w:LsdException Locked="false" Priority="63" Name="Medium Shading 1 Accent 6"/>
<w:LsdException Locked="false" Priority="64" Name="Medium Shading 2 Accent 6"/>
<w:LsdException Locked="false" Priority="65" Name="Medium List 1 Accent 6"/>
<w:LsdException Locked="false" Priority="66" Name="Medium List 2 Accent 6"/>
<w:LsdException Locked="false" Priority="67" Name="Medium Grid 1 Accent 6"/>
<w:LsdException Locked="false" Priority="68" Name="Medium Grid 2 Accent 6"/>
<w:LsdException Locked="false" Priority="69" Name="Medium Grid 3 Accent 6"/>
<w:LsdException Locked="false" Priority="70" Name="Dark List Accent 6"/>
<w:LsdException Locked="false" Priority="71" Name="Colorful Shading Accent 6"/>
<w:LsdException Locked="false" Priority="72" Name="Colorful List Accent 6"/>
<w:LsdException Locked="false" Priority="73" Name="Colorful Grid Accent 6"/>
<w:LsdException Locked="false" Priority="19" QFormat="true"
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<w:LsdException Locked="false" Priority="21" QFormat="true"
Name="Intense Emphasis"/>
<w:LsdException Locked="false" Priority="31" QFormat="true"
Name="Subtle Reference"/>
<w:LsdException Locked="false" Priority="32" QFormat="true"
Name="Intense Reference"/>
<w:LsdException Locked="false" Priority="33" QFormat="true" Name="Book Title"/>
<w:LsdException Locked="false" Priority="37" SemiHidden="true"
UnhideWhenUsed="true" Name="Bibliography"/>
<w:LsdException Locked="false" Priority="39" SemiHidden="true"
UnhideWhenUsed="true" QFormat="true" Name="TOC Heading"/>
<w:LsdException Locked="false" Priority="41" Name="Plain Table 1"/>
<w:LsdException Locked="false" Priority="42" Name="Plain Table 2"/>
<w:LsdException Locked="false" Priority="43" Name="Plain Table 3"/>
<w:LsdException Locked="false" Priority="44" Name="Plain Table 4"/>
<w:LsdException Locked="false" Priority="45" Name="Plain Table 5"/>
<w:LsdException Locked="false" Priority="40" Name="Grid Table Light"/>
<w:LsdException Locked="false" Priority="46" Name="Grid Table 1 Light"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark"/>
<w:LsdException Locked="false" Priority="51" Name="Grid Table 6 Colorful"/>
<w:LsdException Locked="false" Priority="52" Name="Grid Table 7 Colorful"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 1"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 1"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 1"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 1"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 1"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 1"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 1"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 2"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 2"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 2"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 2"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 2"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 2"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 2"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 3"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 3"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 3"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 3"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 3"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 3"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 3"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 4"/>
<w:LsdException Locked="false" Priority="47" Name="Grid Table 2 Accent 4"/>
<w:LsdException Locked="false" Priority="48" Name="Grid Table 3 Accent 4"/>
<w:LsdException Locked="false" Priority="49" Name="Grid Table 4 Accent 4"/>
<w:LsdException Locked="false" Priority="50" Name="Grid Table 5 Dark Accent 4"/>
<w:LsdException Locked="false" Priority="51"
Name="Grid Table 6 Colorful Accent 4"/>
<w:LsdException Locked="false" Priority="52"
Name="Grid Table 7 Colorful Accent 4"/>
<w:LsdException Locked="false" Priority="46"
Name="Grid Table 1 Light Accent 5"/>
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<p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;">Six
weeks ago, we launched the <a href="http://daniellakens.blogspot.com/2020/05/red-team-challenge.html"><span style="color: #1155cc;">Red</span></a><a href="http://daniellakens.blogspot.com/2020/05/red-team-challenge.html"><span style="color: #1155cc;"> </span></a><a href="http://daniellakens.blogspot.com/2020/05/red-team-challenge.html"><span style="color: #1155cc;">Team</span></a><a href="http://daniellakens.blogspot.com/2020/05/red-team-challenge.html"><span style="color: #1155cc;"> </span></a><a href="http://daniellakens.blogspot.com/2020/05/red-team-challenge.html"><span style="color: #1155cc;">Challenge</span></a>: a feasibility study to see whether
it could be worthwhile to pay people to find errors in scientific research. In
our project, we wanted to see to what extent a “Red Team” - people hired to
criticize a scientific study with the goal to improve it - would improve the
quality of the resulting scientific work. </span></p>
<p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;">Currently,
the way that error detection works in science is a bit peculiar. Papers go
through the peer-review process and get the peer-reviewed “stamp of approval”.
Then, upon publication, some of these same papers receive immediate and
widespread criticism. Sometimes this even leads to formal corrections or
retractions. And this happens even at some of the most prestigious scientific
journals. </span></p>
<p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;">So, it
seems that our current mechanisms of scientific quality control leave something
to be desired. Nicholas Coles, Ruben Arslan, and the authors of this post
(Leo Tiokhin and <span style="background: white none repeat scroll 0% 0%; mso-pattern: solid white; mso-shading: white;">Daniël </span>Lakens) were interested in whether Red Teams might
be one way to improve quality control in science. </span></p>
<p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;">Ideally,
a Red Team joins a research project from the start and criticizes each step of
the process. However, doing this would have taken the duration of an entire
study. At the time, it also seemed a bit premature -- we didn’t know whether
anyone would be interested in a Red Team approach, how it would work in
practice, and so on. So, instead, Nicholas Coles, Brooke Frohlich, Jeff Larsen,
and Lowell Gaertner volunteered one of their manuscripts (a completed study
that they were ready to submit for publication). We put out a call on Twitter,
Facebook, and the 20% Statistician blog, and 22 people expressed interest. On
May 15<sup>th</sup>, we randomly selected five volunteers based on five areas
of expertise: Åse Innes-Ker (affective science), Nicholas James
(design/methods), Ingrid Aulike (statistics), Melissa Kline (computational
reproducibility), and Tiago Lubiana (wildcard category). The Red Team was then
given three weeks to report errors.</span></p>
<p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;">Our
Red Team project was somewhat similar to traditional peer review, except that
we 1) compensated Red Team members’ time with a $200 stipend, 2) explicitly
asked the Red Teamers to identify errors in <i>any </i>part of the project
(i.e., not just writing), 3) gave the Red Team full access to the materials,
data, and code, and 4) provided financial incentives for identifying critical
errors (a donation to the <a href="https://www.givewell.org/"><span style="color: #1155cc;">GiveWell</span></a><a href="https://www.givewell.org/"><span style="color: #1155cc;"> </span></a>charity non-profit for each unique “critical
error” discovered). </span></p>
<p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;">The
Red Team submitted 107 error reports. Ruben Arslan--who helped inspire this
project with his <a href="https://rubenarslan.github.io/bug_bounty.html"><span style="color: #1155cc;">Bug</span></a><a href="https://rubenarslan.github.io/bug_bounty.html"><span style="color: #1155cc;">
</span></a><a href="https://rubenarslan.github.io/bug_bounty.html"><span style="color: #1155cc;">Bounty</span></a><a href="https://rubenarslan.github.io/bug_bounty.html"><span style="color: #1155cc;">
</span></a><a href="https://rubenarslan.github.io/bug_bounty.html"><span style="color: #1155cc;">Program</span></a>--served as the neutral arbiter. Ruben
examined the reports, evaluated the authors’ responses, and ultimately decided
whether an issue was “critical” (see <a href="http://www.the100.ci/2020/06/30/red-team-part-2/"><span style="color: #1155cc;">this</span></a><a href="http://www.the100.ci/2020/06/30/red-team-part-2/"><span style="color: #1155cc;"> </span></a><a href="http://www.the100.ci/2020/06/30/red-team-part-2/"><span style="color: #1155cc;">post</span></a> for Ruben’s reflection on the Red Team
Challenge) Of the 107 reports, Ruben concluded that there were 18 unique
critical issues (for details, see <a href="https://osf.io/mdfu4/?view_only=37d533b3f4c74dbb83bff05267a3ea25"><span style="color: #1155cc;">this</span></a><a href="https://osf.io/mdfu4/?view_only=37d533b3f4c74dbb83bff05267a3ea25"><span style="color: #1155cc;"> </span></a><a href="https://osf.io/mdfu4/?view_only=37d533b3f4c74dbb83bff05267a3ea25"><span style="color: #1155cc;">project</span></a><a href="https://osf.io/mdfu4/?view_only=37d533b3f4c74dbb83bff05267a3ea25"><span style="color: #1155cc;"> </span></a><a href="https://osf.io/mdfu4/?view_only=37d533b3f4c74dbb83bff05267a3ea25"><span style="color: #1155cc;">page</span></a>). Ruben decided that any major issues
that potentially invalidated inferences were worth $100, minor issues related
to computational reproducibility were worth $20, and minor issues that could be
resolved without much work were worth $10. After three weeks, the total final
donation was $660. The Red Team detected 5 major errors. These included two
previously unknown limitations of a key manipulation, inadequacies in the
design and description of the power analysis, an incorrectly reported
statistical test in the supplemental materials, and a lack of information about
the sample in the manuscript. Minor issues concerned reproducibility of code
and clarifications about the procedure.</span></p><p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;"><br /></span></p><div class="separator" style="clear: both; text-align: center;"><a href="https://1.bp.blogspot.com/-vImvCe8Oe3U/XvuC-nRNN7I/AAAAAAAAte0/fqoHETfwruESxA7O6hjX2tQzcmD_ntxEQCK4BGAsYHg/s1200/negative-criticism.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="700" data-original-width="1200" height="293" src="https://1.bp.blogspot.com/-vImvCe8Oe3U/XvuC-nRNN7I/AAAAAAAAte0/fqoHETfwruESxA7O6hjX2tQzcmD_ntxEQCK4BGAsYHg/w500-h293/negative-criticism.jpg" width="500" /></a></div>
<p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;"><br /></span></p><p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;">After
receiving this feedback, Nicholas Coles and his co-authors decided to hold off
submitting their manuscript (see <a href="http://www.the100.ci/2020/06/29/red-team-part-1/"><span style="color: #1155cc;">this</span></a><a href="http://www.the100.ci/2020/06/29/red-team-part-1/"><span style="color: #1155cc;"> </span></a><a href="http://www.the100.ci/2020/06/29/red-team-part-1/"><span style="color: #1155cc;">post</span></a> for Nicholas’ personal reflection). They
are currently conducting a new study to address some of the issues raised by
the Red Team. </span></p>
<p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;">We
consider this to be a feasibility study of whether a Red Team approach is
practical and worthwhile. So, based on this study, we shouldn’t draw any
conclusions about a Red Team approach in science except one: it can be done. </span></p>
<p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;">That
said, our study does provide some food for thought. Many people were eager to
join the Red Team. The study’s corresponding author, Nicholas Coles, was
graciously willing to acknowledge issues when they were pointed out. And it was
obvious that, had these issues been pointed out earlier, the study would have
been substantially improved before being carried out. These findings make us
optimistic that Red Teams can be useful and feasible to implement. </span></p>
<p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;">In an
earlier <a href="https://www.nature.com/articles/d41586-020-01392-8"><span style="color: #1155cc;">column</span></a>, the issue was raised that rewarding
Red Team members with co-authorship on the subsequent paper would create a
conflict of interest -- too severe criticism on the paper might make the paper
unpublishable. So, instead, we paid each Red Teamer $200 for their service. We
wanted to reward people for their time. We did not want to reward them only for
finding issues because, before we knew that 19 unique issues would be found, we
were naively worried that the Red Team might find few things wrong with the
paper. In interviews with Red Team members, it became clear that the charitable
donations for each issue were not a strong motivator. Instead, people were just
happy to detect issues for decent pay. They didn't think that they deserved
authorship for their work, and several Red Team members didn't consider
authorship on an academic paper to be valuable, given their career goals. </span></p>
<p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;">After
talking with the Red Team members, we started to think that certain people
might enjoy Red Teaming as a job – it is challenging, requires skills, and
improves science. This opens up the possibility of a freelance services
marketplace (such as<a href="https://en.wikipedia.org/wiki/Fiverr"><span style="color: black; text-decoration: none; text-underline: none;"> </span></a><a href="https://en.wikipedia.org/wiki/Fiverr"><span style="color: #1155cc;">Fiverr</span></a>)
for error detection, where Red Team members are hired at an hourly rate and
potentially rewarded for finding errors. It should be feasible to hire people
to check for errors at each phase of a project, depending on their expertise
and reputation as good error-detectors. If researchers do not have money for
such a service, they might be able to set up a volunteer network where people “Red
Team” each other’s projects. It could also be possible for universities to
create Red Teams (e.g., Cornell University has a<a href="https://ciser.cornell.edu/research/results-reproduction-r-squared-service/"><span style="color: black; text-decoration: none; text-underline: none;"> </span></a><a href="https://ciser.cornell.edu/research/results-reproduction-r-squared-service/"><span style="color: #1155cc;">computational</span></a><a href="https://ciser.cornell.edu/research/results-reproduction-r-squared-service/"><span style="color: #1155cc;"> </span></a><a href="https://ciser.cornell.edu/research/results-reproduction-r-squared-service/"><span style="color: #1155cc;">reproducibility</span></a><a href="https://ciser.cornell.edu/research/results-reproduction-r-squared-service/"><span style="color: #1155cc;"> </span></a><a href="https://ciser.cornell.edu/research/results-reproduction-r-squared-service/"><span style="color: #1155cc;">service</span></a> that researchers can hire). </span></p>
<p class="MsoNormal" style="margin: 12pt 0cm;"><span lang="" style="mso-ansi-language: EN-US;">As
scientists, we should ask ourselves when, and for which type of studies, we
want to invest time and/or money to make sure that published work is as free
from errors as possible. As we continue to consider ways to increase the
reliability of science, a Red Team approach might be something to further
explore.</span></p>Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com1tag:blogger.com,1999:blog-987850932434001559.post-9984142848707552992020-05-11T20:27:00.004+02:002021-03-07T12:17:04.740+01:00Red Team Challenge<i>by Nicholas A. Coles, Leo Tiokhin, Ruben Arslan, Patrick Forscher, Anne Scheel, & Daniël Lakens</i><br />
<br />
<br />
All else equal, scientists should trust studies and theories that have
been more critically evaluated. The more that a scientific product has
been exposed to processes designed to detect flaws, the more that
researchers can trust the product (Lakens, 2019; Mayo, 2018). Yet, there
are barriers to adopting critical approaches in science. Researchers
are susceptible to biases, such as confirmation bias, the “better than
average” effect, and groupthink. Researchers may gain a competitive
advantage for jobs, funding, and promotions by sacrificing rigor in
order to produce larger quantities of research (Heesen, 2018; Higginson
& Munafò, 2016) or to win priority races (Tiokhin & Derex,
2019). And even if researchers were transparent enough to allow others
to critically examine their materials, code, and ideas, there is little
incentive for others--including peer reviewers--to do so. These combined
factors may hinder the ability of science to detect errors and
self-correct (Vazire, 2019). <br />
<br />
Today we announce an initiative that we hope can incentivize critical
feedback and error detection in science: the Red Team Challenge. Daniël
Lakens and Leo Tiokhin are offering a total of $3,000 for five
individuals to provide critical feedback on the materials, code, and
ideas in the forthcoming preprint titled <a href="https://psyarxiv.com/br4y9">“Are facial feedback effects solely driven by demand characteristics? An experimental investigation”</a>.
This preprint examines the role of demand characteristics in research
on the controversial facial feedback hypothesis: the idea that an
individual’s facial expressions can influence their emotions. This is a
project that Coles and colleagues will submit for publication in
parallel with the Red Team Challenge. We hope that challenge will serve
as a useful case study of the role Red Teams might play in science.<br />
<br />
<b>We are looking for five individuals to join “The Red Team”. Unlike
traditional peer review, this Red Team will receive financial incentives
to identify problems. Each Red Team member will receive a $200 stipend
to find problems, including (but not limited to) errors in the
experimental design, materials, code, analyses, logic, and writing. In
addition to these stipends, we will donate $100 to a GoodWell <a href="https://www.givewell.org/charities/top-charities">top ranked charity</a>
(maximum total donations: $2,000) for every new “critical problem”
detected by a Red Team member. Defining a “critical problem” is
subjective, but a neutral arbiter--<a href="https://rubenarslan.github.io/">Ruben Arslan</a>--will
make these decisions transparently. At the end of the challenge, we
will release: (1) the names of the Red Team members (if they wish to be
identified), (2) a summary of the Red Team’s feedback, (3) how much each
Red Team member raised for charity, and (4) the authors’ responses to
the Red Team’s feedback. </b><br />
<div>
<br /></div>
<div>
If you are interested in joining the Red Team, you have until May 14th to <b><span style="background-color: red;">sign up <a href="https://docs.google.com/forms/d/e/1FAIpQLSfyhRD74F2BfcAIfncCeI1Gg5s7jU0K5JhOAvOFRy_1BGGCNA/viewform?usp=sf_link">here</a>.</span></b>
At this link, you will be asked for your name, email address, and a
brief description of your expertise. If more than five people wish to
join the Red Team, we will ad-hoc categorize people based on expertise
(e.g., theory, methods, reproducibility) and randomly select individuals
from each category. On May 15th, we will notify people whether they
have been chosen to join the Red Team.<br />
<br />
For us, this is a fun project for several reasons. Some of us are just
interested in the feasibility of Red Team challenges in science (Lakens,
2020). Others want feedback about how to make such challenges more
scientifically useful and to develop best practices. And some of us
(mostly Nick) are curious to see what good and bad might come from
throwing their project into the crosshairs of financially-incentivized
research skeptics. Regardless of our diverse motivations, we’re united
by a common interest: improving science by recognizing and rewarding
criticism (Vazire, 2019). <br />
<div>
<br /></div>
<div>
<br /></div>
<div>
<br /></div>
<div>
<br /></div>
<br />
<br />
<b>References</b><br />
Heesen, R. (2018). Why the reward structure of science makes reproducibility problems inevitable. <a href="http://philsci-archive.pitt.edu/15575/1/Heesen%202018%20Why%20the%20Reward%20Structure%20of%20Science%20Makes%20Reproducibility%20Problems%20Inevitable.pdf" target="_blank">The Journal of Philosophy</a>, 115(12), 661-674.<br />
Higginson, A. D., & Munafò, M. R. (2016). Current incentives for
scientists lead to underpowered studies with erroneous conclusions. <a href="https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2000995" target="_blank">PLoS Biology</a>, 14(11), e2000995.<br />
Lakens, D. (2019). The value of preregistration for psychological science: A conceptual analysis. <a href="http://team1mile.com/sjpr62-3/wp-content/uploads/2020/03/Lakens_JPR623221-230.pdf" target="_blank">Japanese Psychological Review</a>.<br />
<div>
Lakens, D. (2020). Pandemic researchers — recruit your own best critics. <a href="http://www.nature.com/articles/d41586-020-01392-8" target="_blank">Nature</a>, 581, 121.<br />
Mayo, D. G. (2018). Statistical inference as severe testing. Cambridge: <a href="https://www.cambridge.org/core/books/statistical-inference-as-severe-testing/D9DF409EF568090F3F60407FF2B973B2" target="_blank">Cambridge University Press</a>.<br />
Tiokhin, L., & Derex, M. (2019). Competition for novelty reduces
information sampling in a research game-a registered report. <a href="https://royalsocietypublishing.org/doi/10.1098/rsos.180934" target="_blank">Royal Society Open Science</a>, 6(5), 180934.<br />
Vazire, S. (2019). A toast to the error detectors. <a href="https://www.nature.com/articles/d41586-019-03909-2" target="_blank">Nature</a>, 577(9).</div>
</div>
Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com2tag:blogger.com,1999:blog-987850932434001559.post-2191962108344426832020-03-29T12:00:00.001+02:002021-09-20T19:00:58.964+02:00Effect Sizes and Power for Interactions in ANOVA Designs<div style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 10px; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
Based on our recent <a href="https://doi.org/10.1177/2515245920951503" target="_blank">paper</a> explaining power analysis for ANOVA designs, in this post I want provide a step-by-step mathematical overview of power analysis for interactions. These details often do not make it into tutorial papers because of word limitations, and few good free resources are available (for a paid resource worth your money, see <a href="https://designingexperiments.com/" style="background-color: transparent; box-sizing: border-box; color: #337ab7; text-decoration: none;">Maxwell, Delaney, & Kelley, 2018</a>). This post is a bit technical, but nothing in this post requires more knowedge than multiplying and dividing numbers, and I believe that for anyone willing to really understand effect sizes and power in ANOVA designs digging in to these details will be quite beneficial. There are some take-home messages in this post:</div>
<ol style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; list-style-type: decimal; margin-bottom: 10px; margin-top: 0px; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
<li style="box-sizing: border-box;">In power analyses for ANOVA designs, you should always think of the predicted pattern of means. Different patterns of means can have the same effect size, and your intuition can not be relied on when predicting an effect size for ANOVA designs.</li>
<li style="box-sizing: border-box;">Understanding how patterns of means relate to the effect you predict is essential to design an informative study.</li>
<li style="box-sizing: border-box;">Always perform a power analysis if you want to test a predicted interaction effect, and always calculate the effect size based on means, sd’s, and correlations, instead of plugging in a ‘medium’ partial eta squared.</li>
<li style="box-sizing: border-box;">Crossover interaction effects often have larger effects than ordinal interaction effects and can thus often be studied with high power in smaller samples. If your theory can predict crossover interactions, such experiments might be worthwhile to design.</li>
<li style="box-sizing: border-box;">There are some additional benefits of examining interactions (risky predictions, generalizability, efficiently examining multiple main effects) and it would be a shame if the field is turned away from examining interactions because they sometimes require large samples.</li>
</ol>
<div class="section level1" id="getting-started-comparing-two-groups" style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
<h1 style="box-sizing: border-box; color: inherit; font-family: inherit; font-size: 34px; font-weight: 500; line-height: 1.1; margin: 20px 0px 10px;">
Getting started: Comparing two groups</h1>
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We are planning a two independent group experiment. We are using a validated measure, and we know the standard deviation of our measure is approximately 2. Psychologists are generaly horribly bad at knowing the standard deviation of their measures, even though a very defensible position is that you are not ready to perform a power analysis without solid knowledge of the standard deviation of your measure. We are interested in observing a mean difference of 1 or more, because smaller effects would not be practically meaningful. We expect the mean in the control condition to be 0, and therefore want the mean in the intervention group to be 1 or higher.</div>
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This means the standardized effect size is the mean difference, divided by the standard deviation, or 1/2 = 0.5. This is the Cohen’s d we want to be able to detect in our study:<br />
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position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">−</span><span class="msubsup" id="MathJax-Span-11" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0.25em; padding-right: 0px; padding-top: 0px; padding: 0px 0px 0px 0.25em; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px; width: 1.15em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; clip: rect(3.39em, 1000.7em, 4.2em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: absolute; text-decoration: none; top: -4.01em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-12" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-style: none; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; border-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">m</span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline; left: 0.72em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: absolute; text-decoration: none; top: -3.86em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-13" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-style: none; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; border-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 70.7%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">2</span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; 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border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; clip: rect(3.4em, 1000.53em, 4.2em, -1000em); display: inline; left: 50%; line-height: normal; margin-bottom: 0px; margin-left: -0.26em; margin-right: 0px; margin-top: 0px; margin: 0px 0px 0px -0.26em; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: absolute; text-decoration: none; top: -3.32em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-14" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-style: none; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; border-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">σ<span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; 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border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; clip: rect(0.81em, 1003.6em, 1.24em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: absolute; text-decoration: none; top: -1.28em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-image: none 100% / 1 / 0 stretch; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-style: solid none none; border-top-color: currentColor; border-top-style: solid; border-top-width: 1.3px; border-width: 1.3px 0px 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0em; width: 3.6em;"></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline-block; height: 1.06em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-15" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-style: none; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; border-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0.31em; padding-right: 0px; padding-top: 0px; padding: 0px 0px 0px 0.31em; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">=</span><span class="mfrac" id="MathJax-Span-16" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0.31em; padding-right: 0px; padding-top: 0px; padding: 0px 0px 0px 0.31em; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0.12em; margin-right: 0.12em; margin-top: 0px; margin: 0px 0.12em; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px; width: 2.3em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; clip: rect(3.16em, 1002.16em, 4.33em, -1000em); display: inline; left: 50%; line-height: normal; margin-bottom: 0px; margin-left: -1.09em; margin-right: 0px; margin-top: 0px; margin: 0px 0px 0px -1.09em; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: absolute; text-decoration: none; top: -4.69em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-17" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-18" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-style: none; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; border-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">1</span><span class="mo" id="MathJax-Span-19" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-style: none; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; border-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0.25em; padding-right: 0px; padding-top: 0px; padding: 0px 0px 0px 0.25em; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">−</span><span class="mn" id="MathJax-Span-20" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-style: none; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; border-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0.25em; padding-right: 0px; padding-top: 0px; padding: 0px 0px 0px 0.25em; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">0</span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; clip: rect(3.16em, 1000.47em, 4.19em, -1000em); display: inline; left: 50%; line-height: normal; margin-bottom: 0px; margin-left: -0.25em; margin-right: 0px; margin-top: 0px; margin: 0px 0px 0px -0.25em; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: absolute; text-decoration: none; top: -3.32em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-21" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-style: none; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; border-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">2</span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; clip: rect(0.81em, 1002.3em, 1.24em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: absolute; text-decoration: none; top: -1.28em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-image: none 100% / 1 / 0 stretch; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-style: solid none none; border-top-color: currentColor; border-top-style: solid; border-top-width: 1.3px; border-width: 1.3px 0px 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0em; width: 2.3em;"></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline-block; height: 1.06em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-22" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-style: none; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; border-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0.31em; padding-right: 0px; padding-top: 0px; padding: 0px 0px 0px 0.31em; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">=</span><span class="mn" id="MathJax-Span-23" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-style: none; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; border-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0.31em; padding-right: 0px; padding-top: 0px; padding: 0px 0px 0px 0.31em; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">0.5.</span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; display: inline-block; height: 2.53em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-image: none 100% / 1 / 0 stretch; border-left-color: currentColor; border-left-style: solid; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-style: none none none solid; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border-width: 0px; box-sizing: border-box; display: inline-block; height: 2.62em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: -0.91em; width: 0px;"></span></span> </div>
</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
<span class="math display" style="box-sizing: border-box;"><span class="MathJax_Preview" color="inherit" style="box-sizing: border-box;"></span></span></div>
<div class="MathJax_Display" style="box-sizing: border-box; display: block; margin: 1em 0em; max-height: none; max-width: none; min-height: 0px; min-width: 0px; position: relative; text-align: center; text-indent: 0px; width: 582.994px;">
<span class="MathJax" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mi>d</mi><mo>=</mo><mfrac><mrow><msub><mi>m</mi><mn>1</mn></msub><mo>−</mo><msub><mi>m</mi><mn>2</mn></msub></mrow><mi>σ</mi></mfrac><mo>=</mo><mfrac><mrow><mn>1</mn><mo>−</mo><mn>0</mn></mrow><mn>2</mn></mfrac><mo>=</mo><mn>0.5.</mn></math>" id="MathJax-Element-1-Frame" role="presentation" style="border: 0px none; box-sizing: border-box; direction: ltr; display: inline; float: none; font-size: 14px; font-style: normal; font-weight: normal; letter-spacing: normal; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding: 0px; position: relative; text-align: left; text-indent: 0px; text-transform: none; white-space: nowrap; word-spacing: normal;" tabindex="0"><nobr aria-hidden="true" style="border: 0px; box-sizing: border-box; line-height: normal; margin: 0px; max-height: 5000em; max-width: 5000em; min-height: 0px; min-width: 0px; padding: 0px; text-decoration: none; transition: none 0s ease 0s; vertical-align: 0px; white-space: nowrap !important;"></nobr></span></div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
An independent <i style="box-sizing: border-box;">t</i>-test is mathematically identical to an <i style="box-sizing: border-box;">F</i>-test with two groups. For an <i style="box-sizing: border-box;">F</i>-test, the effect size used for power analyses is Cohen’s <i style="box-sizing: border-box;">f</i>, which is a generalization of Cohen’s d to more than two groups (Cohen, 1988). It is calculated based on the standard deviation of the population means divided by the population standard deviation which we know for our measure is 2), or:</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
<span class="math display" style="box-sizing: border-box;"><span class="MathJax_Preview" color="inherit" style="box-sizing: border-box;"></span></span></div>
<div style="text-align: center;">
<span class="MathJax" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mi>f</mi><mo>=</mo><mfrac><msub><mi>σ</mi><mrow class="MJX-TeXAtom-ORD"><mi>m</mi></mrow></msub><mi>σ</mi></mfrac></math>" face=""helvetica neue" , "helvetica" , "arial" , sans-serif" id="MathJax-Element-2-Frame" role="presentation" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-color: currentcolor; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-style: none; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; border-width: 0px; box-sizing: border-box; color: #333333; direction: ltr; display: inline-table; float: none; font-size: 100%; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: relative; text-align: center; text-decoration: none; text-indent: 0px; text-transform: none; white-space: nowrap; word-spacing: normal;" tabindex="0"><nobr aria-hidden="true" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; white-space: nowrap;"><span class="math" id="MathJax-Span-24" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 3.89em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; font-size: 121%; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 3.18em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(1.25em, 1003.18em, 3.41em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -2.53em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-25" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mi" id="MathJax-Span-26" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">f<span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 1px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0.14em;"></span></span><span class="mo" id="MathJax-Span-27" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0.31em; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">=</span><span class="mfrac" id="MathJax-Span-28" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0.31em; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0.12em; margin-right: 0.12em; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 1.2em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(3.4em, 1001.08em, 4.34em, -1000em); display: inline; left: 50%; line-height: normal; margin-bottom: 0px; margin-left: -0.54em; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -4.69em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="msubsup" id="MathJax-Span-29" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 1.08em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(3.4em, 1000.53em, 4.2em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -4.01em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mi" id="MathJax-Span-30" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">σ<span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 1px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0.03em;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; left: 0.49em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -3.86em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="texatom" id="MathJax-Span-31" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-32" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mi" id="MathJax-Span-33" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 70.7%; font-style: italic; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">m</span></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(3.4em, 1000.53em, 4.2em, -1000em); display: inline; left: 50%; line-height: normal; margin-bottom: 0px; margin-left: -0.26em; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -3.32em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mi" id="MathJax-Span-34" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">σ<span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 1px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0.03em;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(0.81em, 1001.2em, 1.24em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -1.28em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: solid; border-top-width: 1.3px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0em; width: 1.2em;"></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 1.06em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span></span></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 2.53em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: solid; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 2.32em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: -0.91em; width: 0px;"></span></span></nobr><span class="MJX_Assistive_MathML MJX_Assistive_MathML_Block" role="presentation" style="-ms-user-select: none; border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; clip: rect(1px, 1px, 1px, 1px); display: inline; height: 1px; left: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; top: 0px; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px; width: 100%;"><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mi></mi><mo></mo><mfrac><msub><mi></mi><mrow><mi></mi></mrow></msub><mi></mi></mfrac></math></span></span> </div>
where for equal sample sizes,<br />
<br />
<div style="text-align: center;">
<span class="MathJax" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><msub><mi>σ</mi><mrow class="MJX-TeXAtom-ORD"><mi>m</mi></mrow></msub><mo>=</mo><msqrt><mfrac><mrow><munderover><mo>∑</mo><mrow class="MJX-TeXAtom-ORD"><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mrow class="MJX-TeXAtom-ORD"><mi>k</mi></mrow></munderover><mo stretchy="false">(</mo><msub><mi>m</mi><mrow class="MJX-TeXAtom-ORD"><mi>i</mi></mrow></msub><mo>−</mo><mi>m</mi><msup><mo stretchy="false">)</mo><mrow class="MJX-TeXAtom-ORD"><mn>2</mn></mrow></msup></mrow><mi>k</mi></mfrac></msqrt><mo>.</mo></math>" id="MathJax-Element-3-Frame" role="presentation" style="border: 0px none; box-sizing: border-box; direction: ltr; display: inline-table; float: none; font-size: 100%; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding: 0px; position: relative; white-space: nowrap; word-spacing: normal;" tabindex="0"><nobr aria-hidden="true" style="border: 0px none currentcolor; box-sizing: border-box; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; padding: 0px; transition: none 0s ease 0s; vertical-align: 0px;"><span class="math" id="MathJax-Span-35" style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 12.22em;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; font-size: 121%; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 10.09em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(0.31em, 1010.02em, 3.74em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -2.53em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-36" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="msubsup" id="MathJax-Span-37" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 1.08em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.4em, 1000.53em, 4.2em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-38" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">σ<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0.03em;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; left: 0.49em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -3.86em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="texatom" id="MathJax-Span-39" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-40" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-41" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 70.7%; font-style: italic; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">m</span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-42" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.31em; position: static; transition: none 0s ease 0s; vertical-align: 0px;">=</span><span class="msqrt" id="MathJax-Span-43" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.31em; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 7.48em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(2.07em, 1006.38em, 4.88em, -1000em); left: 1.07em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-44" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mfrac" id="MathJax-Span-45" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px 0.12em; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 6.13em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(2.86em, 1006.02em, 4.49em, -1000em); left: 50%; line-height: normal; margin: 0px 0px 0px -3em; padding: 0px; position: absolute; top: -4.8em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-46" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="munderover" id="MathJax-Span-47" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 2.02em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.07em, 1000.85em, 4.45em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mo" id="MathJax-Span-48" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0em;">∑</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.35em, 1000.4em, 4.19em, -1000em); left: 0.91em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.5em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="texatom" id="MathJax-Span-49" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-50" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-51" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 70.7%; font-style: italic; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">k<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0.01em;"></span></span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.35em, 1001.11em, 4.19em, -1000em); left: 0.91em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -3.71em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="texatom" id="MathJax-Span-52" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-53" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-54" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 70.7%; font-style: italic; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">i</span><span class="mo" id="MathJax-Span-55" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 70.7%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">=</span><span class="mn" id="MathJax-Span-56" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 70.7%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">1</span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-57" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">(</span><span class="msubsup" id="MathJax-Span-58" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 0.99em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.39em, 1000.7em, 4.2em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-59" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">m</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; left: 0.72em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -3.86em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="texatom" id="MathJax-Span-60" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-61" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-62" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 70.7%; font-style: italic; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">i</span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-63" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.25em; position: static; transition: none 0s ease 0s; vertical-align: 0px;">−</span><span class="mi" id="MathJax-Span-64" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.25em; position: static; transition: none 0s ease 0s; vertical-align: 0px;">m</span><span class="msubsup" id="MathJax-Span-65" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 0.76em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.16em, 1000.28em, 4.36em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mo" id="MathJax-Span-66" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">)</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; left: 0.33em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.3em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="texatom" id="MathJax-Span-67" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-68" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-69" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 70.7%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">2</span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.15em, 1000.46em, 4.2em, -1000em); left: 50%; line-height: normal; margin: 0px 0px 0px -0.23em; padding: 0px; position: absolute; top: -3.32em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-70" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">k<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0.01em;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(0.81em, 1006.14em, 1.24em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -1.28em; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border-color: currentcolor; border-image: none 100% / 1 / 0 stretch; border-style: solid none none; border-width: 1.3px 0px 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0em; width: 6.13em;"></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1.06em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.01em, 1006.4em, 3.42em, -1000em); left: 1.07em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -5.24em; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 6.4em;"><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 5.9em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 0.39em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 0.82em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 1.24em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 1.66em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 2.09em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 2.51em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 2.93em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 3.36em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 3.78em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 4.21em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 4.63em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 5.05em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixgeneral"; left: 5.48em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;">‾<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(1.33em, 1001.11em, 4.76em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -3.55em; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixsizethreesym"; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">√</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.07em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-71" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">.</span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 2.53em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span><span style="border-color: currentcolor; border-image: none 100% / 1 / 0 stretch; border-style: none none none solid; border-width: 0px; box-sizing: border-box; display: inline-block; height: 3.86em; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: -1.31em; width: 0px;"></span></span></nobr></span></div>
<br />
In this formula <i style="box-sizing: border-box;">m</i> is the grand mean, k is the number of means, and mi is the mean in each group. The formula above might look a bit daunting, but calculating Cohen’s f is not that difficult for two groups.<br />
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
If we take the expected means of 0 and 1, and a standard deviation of 2, the grand mean (the <i style="box-sizing: border-box;">m</i> in the formula above) is (0 + 1)/2 = 0.5. The formula says we should subtract this grand mean from the mean of each group, square this value, and sum them. So we have (0-0.5)^2 and (1-0.5)^2, which are both 0.25. We sum these values (0.25 + 0.25 = 0.5), divide them by the number of groups (0.5/2 = 0.25) and take the square root, we find that <span class="math inline" face=""helvetica neue" , "helvetica" , "arial" , sans-serif" style="box-sizing: border-box; color: #333333; font-size: 14px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"><span class="MathJax" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>σ</mi><mrow class="MJX-TeXAtom-ORD"><mi>m</mi></mrow></msub></math>" id="MathJax-Element-4-Frame" role="presentation" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; direction: ltr; display: inline-table; float: none; font-size-adjust: none; font-size: 100%; font-style: normal; font-weight: normal; letter-spacing: normal; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: relative; text-align: left; text-indent: 0px; text-transform: none; white-space: nowrap; word-spacing: normal;" tabindex="0"><span class="MJX_Assistive_MathML" role="presentation" style="-ms-user-select: none; border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; clip: rect(1px, 1px, 1px, 1px); display: inline; height: 1px; left: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; top: 0px; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px; width: 1px;"><math xmlns="http://www.w3.org/1998/Math/MathML"><msub><mi>σ</mi><mrow><mi>m</mi></mrow></msub></math></span></span></span><span face=""helvetica neue" , "helvetica" , "arial" , sans-serif" style="background-color: white; color: #333333; display: inline; float: none; font-size: 14px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"> </span>= 0.5. We can now calculate Cohen’s f (remember than we know <span class="math inline" style="box-sizing: border-box;"><span class="MathJax" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML"><mi>σ</mi></math>" id="MathJax-Element-5-Frame" role="presentation" style="border: 0px none; box-sizing: border-box; direction: ltr; display: inline; float: none; font-size: 14px; font-style: normal; font-weight: normal; letter-spacing: normal; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding: 0px; position: relative; text-align: left; text-indent: 0px; text-transform: none; white-space: nowrap; word-spacing: normal;" tabindex="0"><span class="MJX_Assistive_MathML" role="presentation" style="border: 0px none; box-sizing: border-box; clip: rect(1px, 1px, 1px, 1px); display: inline; height: 1px; left: 0px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; text-decoration: none; top: 0px; transition: none 0s ease 0s; user-select: none; vertical-align: 0px; width: 1px;"><math xmlns="http://www.w3.org/1998/Math/MathML"><mi>σ</mi></math></span></span></span> = 2 for our measure):</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
<span class="math display" style="box-sizing: border-box;"><span class="MathJax_Preview" color="inherit" style="box-sizing: border-box;"></span></span></div>
<div class="MathJax_Display" style="box-sizing: border-box; display: block; margin: 1em 0em; max-height: none; max-width: none; min-height: 0px; min-width: 0px; position: relative; text-align: center; text-indent: 0px; width: 582.994px;">
<span class="MathJax" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><mi>f</mi><mo>=</mo><mfrac><msub><mi>σ</mi><mrow class="MJX-TeXAtom-ORD"><mi>m</mi></mrow></msub><mi>σ</mi></mfrac><mo>=</mo><mfrac><mn>0.5</mn><mn>2</mn></mfrac><mo>=</mo><mn>0.25</mn></math>" id="MathJax-Element-6-Frame" role="presentation" style="border: 0px none; box-sizing: border-box; direction: ltr; display: inline-table; float: none; font-size: 100%; font-variant-east-asian: normal; font-variant-numeric: normal; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding: 0px; position: relative; white-space: nowrap; word-spacing: normal;" tabindex="0"><nobr aria-hidden="true" style="border: 0px none currentcolor; box-sizing: border-box; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; padding: 0px; transition: none 0s ease 0s; vertical-align: 0px;"><span class="math" id="MathJax-Span-82" style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 11.1em;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; font-size: 121%; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 9.15em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(0.99em, 1009.08em, 3.41em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -2.53em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-83" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-84" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">f<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0.14em;"></span></span><span class="mo" id="MathJax-Span-85" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.31em; position: static; transition: none 0s ease 0s; vertical-align: 0px;">=</span><span class="mfrac" id="MathJax-Span-86" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.31em; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px 0.12em; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 1.2em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.4em, 1001.08em, 4.34em, -1000em); left: 50%; line-height: normal; margin: 0px 0px 0px -0.54em; padding: 0px; position: absolute; top: -4.69em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="msubsup" id="MathJax-Span-87" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 1.08em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.4em, 1000.53em, 4.2em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-88" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">σ<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0.03em;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; left: 0.49em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -3.86em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="texatom" id="MathJax-Span-89" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-90" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-91" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 70.7%; font-style: italic; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">m</span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.4em, 1000.53em, 4.2em, -1000em); left: 50%; line-height: normal; margin: 0px 0px 0px -0.26em; padding: 0px; position: absolute; top: -3.32em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-92" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">σ<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0.03em;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(0.81em, 1001.2em, 1.24em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -1.28em; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border-color: currentcolor; border-image: none 100% / 1 / 0 stretch; border-style: solid none none; border-width: 1.3px 0px 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0em; width: 1.2em;"></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1.06em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-93" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.31em; position: static; transition: none 0s ease 0s; vertical-align: 0px;">=</span><span class="mfrac" id="MathJax-Span-94" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.31em; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px 0.12em; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 1.37em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.14em, 1001.18em, 4.2em, -1000em); left: 50%; line-height: normal; margin: 0px 0px 0px -0.62em; padding: 0px; position: absolute; top: -4.69em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-95" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">0.5</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.16em, 1000.47em, 4.19em, -1000em); left: 50%; line-height: normal; margin: 0px 0px 0px -0.25em; padding: 0px; position: absolute; top: -3.32em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-96" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">2</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(0.81em, 1001.37em, 1.24em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -1.28em; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border-color: currentcolor; border-image: none 100% / 1 / 0 stretch; border-style: solid none none; border-width: 1.3px 0px 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0em; width: 1.37em;"></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1.06em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-97" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.31em; position: static; transition: none 0s ease 0s; vertical-align: 0px;">=</span><span class="mn" id="MathJax-Span-98" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.31em; position: static; transition: none 0s ease 0s; vertical-align: 0px;">0.25</span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 2.53em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span><span style="border-color: currentcolor; border-image: none 100% / 1 / 0 stretch; border-style: none none none solid; border-width: 0px; box-sizing: border-box; display: inline-block; height: 2.63em; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: -0.91em; width: 0px;"></span></span></nobr><span class="MJX_Assistive_MathML MJX_Assistive_MathML_Block" role="presentation" style="border: 0px none; box-sizing: border-box; clip: rect(1px, 1px, 1px, 1px); display: inline; height: 1px; left: 0px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; top: 0px; transition: none 0s ease 0s; vertical-align: 0px; width: 100%;"><math display="block" style="box-sizing: border-box; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s;" xmlns="http://www.w3.org/1998/Math/MathML"></math><br /></span></span></div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
We see that for two groups Cohen’s f is half as large as Cohen’s d, f = 1/2d, which always holds for an <i>F</i>-test with two independent groups.</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
Although calculating effect sizes by hand is obviously an incredibly enjoyable thing to do, you might prefer using software that performs these calculations for you. Here, I will use our <a href="https://aaroncaldwell.us/SuperpowerBook/" target="_blank">Superpower </a>power analysis package (developed by <a href="https://aaroncaldwell.us/" target="_blank">Aaron Caldwell</a> and me). The code below uses a function from the package that computes power analytically for a one-way ANOVA where all conditions are manipulated between participants. In addition to the effect size, the function will compute power for any sample size per condition you enter. Let’s assume you have a friend who told you that they heard from someone else that you now need to use 50 observations in each condition (n = 50), so you plan to follow this trustworthy advice. We see the code below returns a Cohen’s <i style="box-sizing: border-box;">f</i> of 0.25, and also tells us we would have 61.78% power if we use a preregistered alpha level of 0.03 (yes, the alpha level can be set to something else than 0.05 - really).</div>
<pre class="r" style="background-color: whitesmoke; border-radius: 4px; border: 1px solid rgb(204, 204, 204); box-sizing: border-box; color: #333333; display: block; font-family: monospace; font-size: 13px; line-height: 1.42857; margin: 0px 0px 10px; overflow-wrap: break-word; overflow: auto; padding: 9.5px; word-break: break-all;"><code class="hljs" style="background-color: transparent; border-radius: 0px; box-sizing: border-box; color: inherit; font-family: monospace; font-size: inherit; padding: 0px; white-space: pre-wrap;"><span class="hljs-keyword" style="box-sizing: border-box; color: #990000; font-weight: bold;">library</span>(Superpower)
design ← ANOVA_design(
design = <span class="hljs-string" style="box-sizing: border-box; color: #dd1144;">"2b"</span>,
n = <span class="hljs-number" style="box-sizing: border-box; color: #009999;">50</span>,
mu = c(<span class="hljs-number" style="box-sizing: border-box; color: #009999;">1</span>, <span class="hljs-number" style="box-sizing: border-box; color: #009999;">0</span>),
sd = <span class="hljs-number" style="box-sizing: border-box; color: #009999;">2</span>)
power_oneway_between(design, alpha_level = <span class="hljs-number" style="box-sizing: border-box; color: #009999;">0.03</span>)$Cohen_f</code></pre>
<pre style="background-color: white; border-radius: 4px; border: 1px solid rgb(204, 204, 204); box-sizing: border-box; color: #333333; display: block; font-family: monospace; font-size: 13px; line-height: 1.42857; margin: 0px 0px 10px; overflow-wrap: break-word; overflow: auto; padding: 9.5px; word-break: break-all;"><code class="hljs" style="background-color: transparent; border-radius: 0px; box-sizing: border-box; color: inherit; font-family: monospace; font-size: inherit; padding: 0px; white-space: pre-wrap;">## [1] 0.25</code></pre>
<pre class="r" style="background-color: whitesmoke; border-radius: 4px; border: 1px solid rgb(204, 204, 204); box-sizing: border-box; color: #333333; display: block; font-family: monospace; font-size: 13px; line-height: 1.42857; margin: 0px 0px 10px; overflow-wrap: break-word; overflow: auto; padding: 9.5px; word-break: break-all;"><code class="hljs" style="background-color: transparent; border-radius: 0px; box-sizing: border-box; color: inherit; font-family: monospace; font-size: inherit; padding: 0px; white-space: pre-wrap;">power_oneway_between(design, alpha_level = <span class="hljs-number" style="box-sizing: border-box; color: #009999;">0.03</span>)$power</code></pre>
<pre style="background-color: white; border-radius: 4px; border: 1px solid rgb(204, 204, 204); box-sizing: border-box; color: #333333; display: block; font-family: monospace; font-size: 13px; line-height: 1.42857; margin: 0px 0px 10px; overflow-wrap: break-word; overflow: auto; padding: 9.5px; word-break: break-all;"><code class="hljs" style="background-color: transparent; border-radius: 0px; box-sizing: border-box; color: inherit; font-family: monospace; font-size: inherit; padding: 0px; white-space: pre-wrap;">## [1] 61.78474</code></pre>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
We therefore might want to increase our sample size for our planned study. Using the <code style="background-color: rgba(0, 0, 0, 0.04); border-radius: 4px; box-sizing: border-box; color: inherit; font-family: monospace; font-size: 12.6px; padding: 2px 4px; white-space: pre;">plot_power</code> function, we can see we would pass 90% power with 100 observations per condition.</div>
<pre class="r" style="background-color: whitesmoke; border-radius: 4px; border: 1px solid rgb(204, 204, 204); box-sizing: border-box; color: #333333; display: block; font-family: monospace; font-size: 13px; line-height: 1.42857; margin: 0px 0px 10px; overflow-wrap: break-word; overflow: auto; padding: 9.5px; word-break: break-all;"><code class="hljs" style="background-color: transparent; border-radius: 0px; box-sizing: border-box; color: inherit; font-family: monospace; font-size: inherit; padding: 0px; white-space: pre-wrap;">plot_power(design, alpha_level = <span class="hljs-number" style="box-sizing: border-box; color: #009999;">0.03</span>, min_n = <span class="hljs-number" style="box-sizing: border-box; color: #009999;">45</span>, max_n = <span class="hljs-number" style="box-sizing: border-box; color: #009999;">150</span>)$plot_ANOVA</code></pre>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
<img 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" 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<div class="section level1" id="interaction-effects" style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
<h1 style="box-sizing: border-box; color: inherit; font-family: inherit; font-size: 34px; font-weight: 500; line-height: 1.1; margin: 20px 0px 10px;">
Interaction Effects</h1>
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So far we have explained the basics for effect size calculations (and we have looked at statistical power) for 2 group ANOVA designs. Now we have the basis to look at interaction effects.</div>
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One of the main points in this blog post is that it is better to talk about interactions in ANOVAs in terms of the pattern of means, standard deviations, and correlations, than in terms of a standarized effect size. The reason for this is that, while for two groups a difference between means directly relates to a Cohen’s d, wildly different patterns of means in an ANOVA will have the same Cohen’s <i style="box-sizing: border-box;">f</i>. In my experience helping colleagues out their with power analyses for ANOVA designs, talking about effects in terms of a Cohen’s <i style="box-sizing: border-box;">f</i> is rarely a good place to start when thinking about what your hypothesis predicts. Instead, you need to specify the predicted pattern of means, have some knowledge about the standard deviation of your measure, and then calculate your predicted effect size.</div>
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There are two types of interactions, as visualized below. In an ordinal interaction, the mean of one group (“B1”) is always higher than the mean for the other group (“B2”). Disordinal interactions are also known as ‘cross-over’ interactions, and occur when the group with the larger mean switches over. The difference is important, since another main takeaway of this blog post is that, <i>in two studies where the largest simple comparison has the same effect size</i>, a study with a disordinal interaction has much higher power than a study with an ordinal interaction (note that an ordinal interaction can have a bigger effect than a disordinal one - in general it is not just about the pattern of means, but also how much means differ!). Thus, if possible, you will want to design experiments where an effect in one condition flips around in the other condition, instead of an experiment where the effect in the other condition just disappears. I personally never realized this before I learned how to compute power for interactions, and never took this simple but important fact into account. Let’s see why it is important.</div>
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<div class="section level1" id="calculating-effect-sizes-for-interactions" style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
<h1 style="box-sizing: border-box; color: inherit; font-family: inherit; font-size: 34px; font-weight: 500; line-height: 1.1; margin: 20px 0px 10px;">
Calculating effect sizes for interactions</h1>
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<img 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br39XxP6Xy1zfaLVuleyHPSCUmBCkfD61BYZUvpfLXN9otW6V1IhXRCUqBC774qO0rnq222X7RK70IqpBOSAhWOh9edr9pPYDpfbbP9olU6F1IjnZAUqHDJ106EpUBvZvtFq3QupEY6ISlQ4WR4vfmq7Sedr7bZftEqnQupkU5IClS46GsXwqrbSeerbbZftErfQqqkE5ICFfr2Vd1NOl9ts/2iVfoWUiWdkBSocDq8roTVN5POV9tsv2iVroXUSSckBSp07SsFWpDtF63StZA66YSkQIWz4fXkq76XdL7aZvtFq3QtpE46ISlQ4Yqv6YW9sJV0vtpm+0Wr9CzkBdIJSYEK58Prx9cLO0nnq222X7RKz0JeIJ2QFKhwzdfkwl7aSDpfbbP9olU6FvIS6YSkQIWOfaVAi7L9olU6FvIS6YSkQAVleL0Ie2kb6Xy1zfaLVulYyEukE5ICFfr19eI20vlqm+0XrdKvkBdJJyQFKmjD60PYi5tI56tttl+0Sr9CXiSdkBSo0K+vFGhZtl+0Sr9CXiSdkBSooA6vB18v/9Cl89U22y9apVshL5NOSApUuOFrXmEvbyGdr7bZftEq3Qp5mXRCUqCCPrz8vl75kUvnq222X7RKr0JeIZ2QFKhwy9eswlKgpdl+0Sq9CnmFdEJSoEKvvl7ZQDpfbbP9olV6FfIK6YSkQIULw8su7LX1p/PVNtsvWqVTIa+RTkgKVOjU12vLT+erbbZftEqnQl4jnZAUqHBpeLl9vfrjls5X22y/aJU+hbxKOiEpUOG2rxmFpUDLs/2iVfoU8irphKRAhYvDS+3r1cWn89U22y9apU8hr5JOSApUKPA1n7DX157OV9tsv2iVLoW8TjohKVChS1+vLz2dr7bZftEqXQp5nXRCUqDC5eHlFfbGytP5apvtF63So5A3SCckBSr06CsFOiTbL1qlRyFvkE5IClS4Mry0wt5YdzpfbbP9olV6FPIG6YSkQIUOfb217nS+2mb7Rat0KOQt0glJgQrXhpfU11vLTuerbbZftEqHQt4inZAUqFDmayZhb646na+22X7RKv0JeZN0QlKgwtXhpfSVAh2W7Ret0p+QN0knJAUqFPqaSNiba07nq222X7RKf0LeJJ2QFKjQna+315zOV9tsv2iV7oS8TTohPQv0i1QcCOu9lFLyrfiLL1wL1Hvzg0goZEYCF6hftM71s1++M37BitOd8G2z/aJVehOygHRCUqDCjeGl85UCHZrtF63Sm5AFpBOSAhWKfU0ibMF60/lqm+0XrdKbkAWkE5ICFW4NL5mvJT9f6Xy1zfaLVulMyBLSCUmBCuW+phC2ZLXpfLXN9otW6UzIEtIJSYEKfflatNp0vtpm+0Wr9CVkEemEpECFm8NLJSwFOjzbL1qlLyGLSCckBSr05WvRWtP5apvtF63Sl5BFpBOSAhVuDy+RsGVLTeerbbZftEpXQpaRTkgKVOjK17KVpvPVNtsvWqUrIctIJyQFKhQML42vhT9Z6Xy1zfaLVulJyELSCUmBCoN8DS4sBVqT7Ret0pOQhaQTkgIVSoaXxdfCdabz1TbbL1qlJyELSSckBSoM8zW0sKXLTOerbbZftEpHQpaSTkgKVOjI19JVpvPVNtsvWqUjIUtJJyQFKhQNL4ewFGhVtl+0SkdClpJOSApU6MfX4kWm89U22y9apR8hi0knJAUqlA0vg6/Fa0znq222X7RKP0IWk05IClQY6mtYYcuXmM5X22y/aJVuhCwnnZAUqFA4vPi+UqCV2X7RKt0IWU46ISlQYbCvUYUtX2A6X22z/aJVuhGynHRCUqBCL74OWGA6X22z/aJVehFyAOmEpECF0uFFF3bA8tL5apvtF63Si5ADSCckBSp04uuQ5aXz1TbbL1qlEyGHkE5IClQoHl5sYSnQ6my/aJVOhBxCOiEpUKETX4csLp2vttl+0SqdCDmEdEJSoEL58CL7OuiHKZ2vttl+0Sp9CDmIdEJSoEKNr/GEHbS0dL7aZvtFq/Qh5CDSCUmBCgOGF9fXYT9K6Xy1zfaLVulCyGGkE5ICFap8jSYsBToi2y9apQshh5FOSApU6MLXYQtL56tttl+0ShdCDiOdkBSoMGR4UYUduK50vtpm+0Wr9CDkQNIJSYEKPfg6cFnpfLXN9otW6UHIgaQTkgIVBg0vpq9Df4zS+Wqb7Ret0oGQQ0knJAUqVPoaSVgKdFS2X7RKB0IOJZ2QFKgwbHghfR26qHS+2mb7Rat0IORQ0glJgQq1vsYRdvCa0vlqm+0XrZJfyMGkE5ICFfL7OnhJ6Xy1zfaLVskv5GDSCUmBCgOHF0/Y4StK56tttl+0Snohh5NOSApUSO8rBToy2y9aJb2Qw0knJAUqDB1eOGGHryedr7bZftEq6YUcTjohKVAhu68V60nnq222X7RKdiErSCckBSoMHl4wXyuWk85X22y/aJXsQlaQTkgKVBjhawRha1aTzlfbbL9oleRC1pBOSApUGD68UL5SoKOz/aJVkgtZQzohKVBhjK8BhK1ZSzpfbbP9olWSC1lDOiEpUCG3r1VrSeerbbZftEpuIatIJyQFKlQML5CwVStJ56tttl+0Sm4hq0gnJAUq5PaVAh2f7RetklvIKtIJSYEKNcML42vdT046X22z/aJVUgtZRzohKVBhnK/OwtatI52vttl+0SqphawjnZAUqFA1vCC+Vv7cpPPVNtsvWiWzkJWkE5ICFUb66iosBdoi2y9aJbOQlaQTkgIVMvtauYp0vtpm+0WrZBayknRCUqBC3fBCCFu7iHS+2mb7RaskFrKWdEJSoEJiX2vXkM5X22y/aJXEQtaSTkgKVKgcXgBhq5eQzlfbbL9olbxCVpNOSApUyOsrBdom2y9aJa+Q1aQTkgIVaofn72v1CtL5apvtF62SV8hq0glJgQrjfXW6N+sXkM5X22y/aJW0QtaTTkgKVKgenrev9fnpfLXN9otWSStkPemEpECFBr663J0j4tP5apvtF62SVcgRpBOSAhWy+kqBtsr2i1bJKuQI0glJgQr1w/MVdkR4Ol9ts/2iVbIKOYJ0QlKgQlJfx4Sn89U22y9aJamQY0gnJAUqjBhejAId/r3pfLXN9otWSSrkGNIJSYEKTXyd/A4dFZ3OV9tsv2iVnEKOIp2QFKgwZngUaCMoUCGnkKNIJyQFKrTxdep7dFRwOl9ts/2iVXIKOYp0Qra4d9989HC5/OBT+fjxcse3f3V39+qR/PfRyhpENyWlr+OC0/laRKWQ4YxMKeQ40gnZ4N7d+ig6nvj64uEMCtRN2HGx6XwtoVbIcEamFHIc6YRscOc+WT749O7l4+WDz08+8eLhez+//+f58jv6ysZHtyWjryNj0/laQq2Q4YzMKORI0gk5/s598XB9Jn/1aC3nAffn/Q9X/z7Z/HO+stHRjRk3PB9hKdAzqoUMZ2RGIUeSTsjx9+2z7fn82amWzzaXAG8en4q8W9no6MZk9HVkaDpfC6gWMpyRGYUcSTohx9+3T5Y/W/97+sDo1aPNJ149evD3P10u//zT02+MpuvY4Xn4OvaHJJ2vBVQLGc7IhEKOJZ2Qo+/b/fn8xcPj55x2FwK7p+y3Wq+XtOGLvjgoM4fMySKNGV2gI4TszEgPIfvDq0D3T0E9Xy5/+Pndv3y03D9w6lNXjzbr8EfErEALhOzNyP7scGDCAj16Wcjz3S9Bdyf+s6fuoz1gGv3wYfonnUYnpnvEdJt6IcMZmU/I0aQT0uoK9M1jeYS04fnpy0qi6ZrQ19GB6Xy9Tb2Q4YzMJ+Ro0glpVaAnp3/tlmi6jh/e1MKOz0vn623qhQxnZDohx5NOSKvfwp+/WPn0Of1wuubzdXxcOl8LqBYynJHphBxPOiFbvA70w6N/N+yfYNo/dDozOJquDYY3ra8NfjzS+VpAtZDhjMwmZAPSCWn0l0gHL1Z+svH0/DmoaLo29XUKYSlQlWohwxmZTcgGpBNy/P16L+L7Z396/OrR/vmlFw9Xrxp5+dPwT9m3GJ5TgVYfIp2vBVQLGc7IbEI2IJ2QDe7XlwdvfrN9u4aj55eebd/75vQvP6Lp2tZXe2FbZKXztYRaIcMZmUzIFqQTssXd+vKjex0/WPu58/XoJSIv/3K5fO+Hp6f7cLpm87VFVDpfi6gUMpyRyYRsQToheUd6ocXwJhSWAm2e7RetkkzIFqQTkgIVcvnaJCmdr7bZftEquYRsQjohKVChyfAmE7ZJTjpfbbP9olVyCdmEdEJSoEIqX9vkpPPVNtsvWiWVkG1IJyQFKrQZHgU6CgpUSCVkG9IJSYEKrX01vW/bpKTz1TbbL1ollZBtSCckBSo0Gt4kvjb6qUjnq222X7RKJiEbkU5IClRo7qvhndsoI52vttl+0SqZhGxEOiEpUCGRr60y0vlqm+0XrZJIyFakE5ICFVoNbwJhKVCTbL9olURCtiKdkBSokMjXVgnpfLXN9otWSSRkK9IJSYEKzYZn7muzn4h0vtpm+0Wr5BGyGemEpEAFA1+N7t5mx0/nq222X7RKHiGbkU5IClRoNzxjX9v9PKTz1TbbL1oljZDtSCckBSpY+Gpy/1KgRtl+0SpphGxHOiEpUCGNr+2Ons5X22y/aJU0QrYjnZAUqNBweKbCNjx4Ol9ts/2iVbII2ZB0QlKgQhZfGx47na+22X7RKlmEbEg6ISlQoeXwDIVteeh0vtpm+0WrJBGyJemEpECFJL5SoGbZftEqSYRsSTohKVCh6fCmKNDxx0rnq222X7RKEiFbkk5IClQw8rXxXdz0wOl8tc32i1bJIWRT0glJgQpth2dfoA0Ols5X22y/aJUcQjYlnZAUqGDla9P7uO1h0/lqm+0XrZJCyLakE5ICFVL4SoEaZvtFq6QQsi3phKRAhcbDsxG27UHT+Wqb7RetkkLItqQTkgIVMvja+KDpfLXN9otWySBkY9IJSYEKrYdnIWzjQ6bz1TbbL1olg5CNSSckBSok8LX1IdP5apvtF62SQMjWpBOSAhWaD48CHQ4FKiQQsjXphKRABUNfW93NrQ+YzlfbbL9olQRCtiadkBSo0H54rX1t/gOQzlfbbL9olfhCNiedkBSoYOlrm/u5uf/pfLXN9otWiS9kc9IJSYEK4X1tr386X22z/aJVwgvZnnRCUqCCwfDaCkuBGmf7RauEF7I96YSkQIXwvraXP52vttl+0SrhhWxPOiEpUMFieC19NZA/na+22X7RKtGFNCCdkBSoYOvr+HvawP10vtpm+0WrRBfSgHRCUqCCyfAo0GFQoEJ0IQ1IJyQFKhj7Ovautrh2SOerbbZftEpwIS1IJyQFKgT31UL8dL7aZvtFqwQX0oJ0QlKggs3wWglrIn46X22z/aJVYgtpQjohKVAhtq8UqH22X7RKbCFNSCckBSoYDa+RsCbap/PVNtsvWiW2kCakE5ICFUL7amN9Ol9ts/2iVUILaUM6ISlQwWp4jQu02bruEvpqm+0XrRJaSBvSCUmBCva+1t/bRlcN6Xy1zfaLVokspBHphKRABbPhUaDlUKBCZCGNSCckBSpM4Gv13W2kfDpfbbP9olUiC2lEOiEpUCGwr1bGp/PVNtsvWiWwkFakE5ICFeyGN1pYK+HT+Wqb7RetElhIK9IJSYEKcX018z2dr7bZftEqcYU0I52QFKhgODwKtBQKVIgrpBnphKRAhUl8rbrDzXRP56tttl+0SlwhzUgnJAUqWA5vlK92tqfz1TbbL1olrJB2pBOSAhWm8bXiHreTPZ2vttl+0SphhbQjnZAUqBDVV0PX0/lqm+0XrRJVSEPSCUmBCqbDGyEsBTpVtl+0SlQhDUknJAUqRPXV0PR0vtpm+0WrRBXSkHRCUqCC7fCqhbU0PZ2vttl+0SpBhbQknZAUqBDUV0vP0/lqm+0XrRJUSEvSCUmBCsbDq/TV1PN0vtpm+0WrxBTSlHRCUqDCZL4OutMp0Omy/aJVYgppSjohKVDBenhjC9RgSel8tc32i1aJKaQp6YSkQIXpfB1wr9teJqTz1TbbL1olpJC2pBOSAhVC+mrreDpfbbP9olVCCmlLOiEpUMF8eBXCGjuezlfbbL9olYhCGpNOSM8C/WJuHPhq+C25cS1Q781PzezsqiBwgfpF69if/cZcgZosKN0J3zbbL1olopDGpBOSAhWm9LXwfrd+jJXOV9tsv2iVgEJak05IClSYYHj1BWqznnS+2mb7RasEFNKadEJSoMKkvpbd8RTopNl+0SoBhbQmnZAUqBDPV3O90/lqm+0XrRJPSHPSCUmBClMMb5iw5nan89U22y9aJZ6Q5qQTkgIVwvlqL3c6X22z/aJVwglpTzohKVBhkuENEZYCnTjbL1olnJD2pBOSAhXC+WqvdjpfbbP9olXCCWlPOiEpUGGa4ZX7OoHa6Xy1zfaLVokm5ASkE5ICFab29dZ9P4/QFyIAACAASURBVIHZ6Xy1zfaLVokm5ASkE5ICFSYaXqmvU4idzlfbbL9olWBCTkE6ISlQYXJfr9/5FOjk2X7RKsGEnIJ0QlKgQjBfp9A6na+22X7RKsGEnIJ0QlKgwlTDKxN2Eq3T+Wqb7RetEkvISUgnJAUqxPJ1EqnT+Wqb7RetEkvISUgnJAUqTDa8El+nkTqdr7bZftEqoYSchnRCUqCCg6+X734K1CHbL1ollJDTkE5IClSYbniDCtRyIel8tc32i1YJJeQ0pBOSAhU8fL10/090TZDOV9tsv2iVSEJORDohKVAhkq8TCZ3OV9tsv2iVSEJORDohKVBhwuHdEnYqodP5apvtF60SSMipSCckBSoE8pUCdcn2i1YJJORUpBOSAhWmHN4NYafSOZ2vttl+0SqBhJyKdEJSoEIcXyezOZ2vttl+0SpxhJyMdEJSoMKkwyssUONVpPPVNtsvWiWOkJORTkgKVHDy9XwE010NpPPVNtsvWiWMkNORTkgKVJh2eBSoCgUqhBFyOtIJSYEKXr6ezWA6ldP5apvtF60SRsjpSCckBSpE8XVCk9P5apvtF60SRcgJSSckBSpMPLyLwk4ocjpfbbP9olWiCDkh6YSkQIUgvk7pcTpfbbP9olWCCDkl6YSkQIWph0eBKlCgQhAhpySdkBSo4OfrQr/ZfgnpfLXN9otWCSLklKQTkgIVJh+e6uukFqfz1TbbL1olhpCTkk5IClRw9HWh3TjBCtL5apvtF60SQ8hJSSckBSqE8HVah9P5apvtF60SQshpSSckBSpMPzxFWArUMdsvWiWEkNOSTkgKVAjh67QGp/PVNtsvWiWEkNOSTkgKVHAY3pmwExuczlfbbL9olQhCTkw6ISlQIYKvE/ubzlfbbL9olQhCTkw6ISlQwWN4FOgJFKgQQciJSSckBSr4+rpQPjQnna+22X7RKgGEnJp0QlKggsvwLhXoNOnpfLXN9otWCSDk1KQTkgIVnH1dOJz+0/lqm+0XreIv5OSkE5ICFfx9pUAp0AP8hZycdEJSoILP8A6FndzddL7aZvtFq/gLOTnphKRABXdfp1c3na+22X7RKu5CTh+eTkgKVHAanmN/5vPVNtsvWsVdyOmz0wlJgQruvlKgFOgh/kJOnp1OSApU8BoeBXoABSr4Czl5dDohKVDB39fJvU3nq222X7RKACGnjk4nJAUqBPCVAqVAhQBCTh2dTkgKVHAbnlt/5vPVNtsvWiWCkBMnpxOSAhUi+EqBUqB7Igg5cXI6ISlQwW94FOgeClQIIeS0wemEpECFEL5Oq2w6X22z/aJVQgg5bXA6ISlQwXF4FOgOClQIIeS0uemEpECFEL5Oa2w6X22z/aJVYgg5aW46ISlQwXN4FOgWClSIIeSksemEpECFGL5SoH7ZftEqCBkACrQYfA0ABSogZAAo0GJiPOXEL5H8sv2iVVyng5AbKNBiKNAAUKACBRoACrSYEC+7w1fHbL9oFd/pIOQaCrSYKAXKXyJ5ZftFq0QpUIS8DAUqUKABoEAF5+kg5IpxBfrVf/ms4VqOiaZriPdumFjYdL5aChnOyDAFipAXuVWg3337Jw0Xc0Q0XSnQCNwqUEMhwxlJgQZgbIEuFn9y65z/5qOHy+UHnx7c8urRcs23f6V/frOyG4ednADvXzu1sOl8tRQynJHe00HIu7EF+vqT+zvv7V9c/ZqtnBs3N7x4eOCr8vnNyq5HTw8FGoAbvloKGc5I7+kg5N34XyL97nuLxVv/8dpXPFk++PTu5ePlg8/3Nz1ffufq5zcruxU9NYEKlP9f+IvYCRnOSPfpIGSD38K//h/Xz/kvHm5P6+/9fH/bk+WHVz+/WdnN6Inx/3+RxdeC38KbCRnOSPfpIGSTlzGtz/l/dumzz7Yn92fi6JvHB24qn9+urCB6UtwLdPoXjqTzdY2RkOGM9J8OQjZ5Hejrv3l3sfj6hXP+k+XP1v8ePEp69ejB3/90ufzzTy98fruykugp8RneQi3QiYRN5+sGGyHDGek/HYRs9EL63/3F/Tn/3275d4e/Bd2f3F883D+ntHvKfmWq8vk/3fIF3HPg6OEH3svyovCF9CZCYuQZCNnqL5E+kbvya7+84evz5fKHn9/9y0fL+09RoDc4UnQxe2GL/xLJQEiMPAMh2xTob7+3uO3r/nUhu2eZVk/da5/falsWPR0uDx+OHyRN/ZAp3SOmHRZChjMywHQQsslzoKuz/YVn7bUT/o7nywefX/58NF29C/Tkw0mETefrBhshwxkZYToIOb5Af7M623/zwp9/XPN1dY6nQK9DgR5TUqBGQoYzMsJ0EHL060DXZ/sfX/z85V9qbhTlt/DXOPMTX299hZmQ4YwMMR2EvPrpm/fKr9+9crZfsXs5nbys7s3jQ0XPP79b2a3oqfEt0LMbphA2na+WQoYzMsR0EPLqp2/9LfxfXD/b36l/2PFkc27feMtfIl1BsRNfr2EpZDgjY0wHIa9R8G5M1872d2sr3z/50+IXD1evGnn50/VNyue3K7sePT3RCnQCYdP5ailkOCNjTAchr3H7/UCvnu1XvDx4c5sXD9en9Wfb97759PTzRyu7deCpcS1Q5SZ8PcdSyHBGxpgOQl6jxTvSv/zoXscP1qfzra93L/9yuXzvh5+fff5oZbePPC3TD091c0ph0/lqKWQ4I4NMByGvwP8nkuBZoOqN+Dpxtl+0SpDpIOQVKFBh8uFdMHNCYdP5apvtF60SZToIeRkKVKBAA0CBClGmg5CXoUAFxwItu7096Xy1zfaLVgkzHYS8CAUqTD28iyf26c746Xy1zfaLVgkzHYS8CAUq+BVo+Wcak85X22y/aJU400HIS1CgwsTDu3Jan+yMn85X22y/aJU400HIS1CgAgUaAApUiDMdhLwEBSq4Fei1z9kOKJ2vttl+0SqBpoOQF6BAhWmHd1VJfHXJ9otWCTQdhLwABSp4FejwzzYjna+22X7RKpGmg5A6FKgw6fBunNInOuOn89U22y9aJdJ0EFKHAhUiFehEZ/x0vtpm+0WrhJoOQqpQoIJTgd76vOWI0vlqm+0XrRJqOgipQoEKUw7vpo746pDtF60SajoIqUKBCj4FevsrDGeUzlfbbL9olVjTQUgNClSYcHgFMuLr9Nl+0SqxpoOQGhSoEKtAJxE2na+22X7RKsGmg5AKFKjgUqAlX4OvE2X7RasEmw5CKlCgwnTDK1MRX6fO9otWiTYdhDyHAhU8CrTsq8ymlM5X22y/aJVo00HIcyhQYbLhlYqIrxNn+0WrhJsOQp5BgQqBC9RqTOl8tc32i1YJNx2EPIMCFRwKtPTr8HWSbL9olXDTQcgzKFBhquGVa2gubDpfbbP9olXiTQchT6FAhekLtPwr8XWKbL9olXjTQchTKFAhXoGaP2ufzlfbbL9olXjTQchTKFBhouENOYtbn/HT+Wqb7RetEm86CHkKBSpMXqCtv3g46Xy1zfaLVok3HYQ8hQIVphnesHO48Rk/na+22X7RKvGmg5CnUKACBRoAClSINx2EPIUCFaYu0IFfbjGqdL7aZvtFq8Sbzh1CnkCBCpMMb6h/+Dphtl+0Srzp3CHkCRSoMHGBDv4Gg1ml89U22y9aJd50ViDkIRSoMMXwhtuHr9Nl+0WrxJvOCoQ8hAIVYhao6QtH0vlqm+0XrRJvOmsQ8gAKVJi2QCu+pf2w0vlqm+0XrRJvOmsQ8gAKVJhgeFXu4etU2X7RKvGmswEhBQpUmLRAq76p+bTS+Wqb7RetEm86GxBSoEAF++HVmYevU2X7RavEm84GhBQoUCFqgRoKm85X22y/aJV409mCkHsoUGHKAq38ttn7apvtF60SbzpbEHIPBSqYD6/aO3ydJtsvWiXedHYg5A4KVJiwQKu/sfG80vlqm+0XrRJvOjsQcgcFKlgPb4R1+DpJtl+0Srzp7EHILRSokKJA2w4sna+22X7RKvGmswcht1CgwnQFOuJbZ+6rbbZftEq86exByC0UqGA8vFHOGQmbzlfbbL9olXjTERByAwUqTFago7553r7aZvtFq8SbjoCQGyhQwXZ4I42zETadr7bZftEq8aZzAEKuoUAFCjQAFKgQbzoHIOQaClSYqkB9vl8lna+22X7RKvGmcwhCrqBABdPhjT5hm5zx0/lqm+0XrRJvOocg5AoKVJioQP2OcE46X22z/aJV4k3nCIS8o0APsRxeg9O1xRk/na+22X7RKvGmcwRC3lGgh1CgAaBAhXjTOQIh7yjQQ6Yp0BbHaDa1dL7aZvtFq8SbzjEISYEeYji8Jqrhq3W2X7RKvOkcg5AU6CGTFGibo7QaWzpfbbP9olXiTecEhKRAD7AbXiPR8NU42y9aJd50TkBICvSA8AXa/oUj6Xy1zfaLVok3nVMQkgIVpijQVsdpNLd0vtpm+0WrxJvOKQhJgQpmw2un2ex9tc32i1aJN50zZi8kBSpMUKDtjtRmcOl8tc32i1aJN50zZi8kBSpQoAGgQIV40zlj9kJSoILV8FpK1ljYdL7aZvtFq8SbzjlzF5ICFewLtOWx5umrbbZftEq86ZwzdyE9C/SLeXCgWNOjNThYQFwL1HvzGZm5kFyBCkZnv7bn6LZHS3fCt832i1aJNx2FmQtJgQrmBRrvcOl8tc32i1aJNx2NeQtJgQo2w2v8JFHb46Xz1TbbL1ol3nQ05i0kBSpYF2jr483RV9tsv2iVeNPRmLeQFKhgMrzWF6Btj5jOV9tsv2iVeNNRmbWQFKhAgQaAAhXiTUdl1kJSoIJxgRoccvQx0/lqm+0XrRJvOjpzFpICFSyG1/4CdN6+2mb7RavEm47OnIWkQAXbAg150HS+2mb7RavEm84FZiwkBSoYDM/iArTlUdP5apvtF60SbzoXmLGQFKiQpkDbnfHT+Wqb7RetEm86l5ivkBSoYFqgRocdedx0vtpm+0WrxJvOJeYrJAUqtB+e0QXojH21zfaLVok3nUvMV0gKVLAsULMDjztyOl9ts/2iVeJN5yKzFZICFZoPz+oCdMa+2mb7RavEm85FZiskBSokKtBW17bpfLXN9otWiTedy8xVSApUMCzQxgdu1s3pfLXN9otWiTedy8xVSApUaD08wwvQVuWczlfbbL9olXjTucJMhaRABbsCbXvck4OPOXo6X22z/aJV4k3nCjMVkgIVGg/P9AJ0rr7aZvtFq8SbzhVmKiQFKqQq0DaHT+erbbZftEq86VxjnkJSoIJZgTY97Pnh5+SrbbZftEq86VxjnkJSoELb4RlfgLYJSOerbbZftEq86VxllkJSoIJVgbY8qhowI19ts/2iVeJN5yqzFJICFZoOz/wCtElFp/PVNtsvWiXedK4zRyEpUCFvgVZHpPPVNtsvWiXedK4zRyEpUMGoQBsetHlGOl9ts/2iVeJN5wYzFJICFVoOb4IL0BYh6Xy1zfaLVok3nRvMUEgKVLAp0HbHvBIyG19ts/2iVeJN5wYzFJICFdIV6Hhh0/lqm+0XrRJvOreYn5AUqNBweJM8gp+jr7bZftEq8aZzi/kJSYEKJgXa7JAmOel8tc32i1aJN52bzE5IClRoN7yJLkDHB6Xz1TbbL1ol3nRuMjshKVAhYYGOPeOn89U22y9aJd50bjM3ISlQwaJAWx3xdlJVVDpfbbP9olXiTec2cxOSAhWaDW+6C9DZ+Wqb7RetEm86t5mbkBSoYFCgjQ5YlFUTls5X22y/aJV40ylgZkJSoEKr4U14ATo7X22z/aJV4k2ngJkJSYEKKQt0XFo6X22z/aJV4k2nhHkJSYEK7Qu0zfFK0+bgq222X7RKvOmUMC8hKVCh0fCmvQAd19fpfLXN9otWiTedImYlJAUqNC/QJocbkDc8MJ2vttl+0SrxplPErISkQIU2w5v6AnRUY6fz1TbbL1ol3nTKmJOQFKiQv0AHJ6bz1TbbL1ol3nTKmJOQFKjQukBbHG1YYv++2mb7RavEm04ZcxKSAhWaDG/6C9Axmel8tc32i1aJN51CZiQkBSo0LtAGBxuc2b2vttl+0SrxplPIjISkQIUWw/O4AB3R2ul8tc32i1aJN51S5iMkBSr0UKADU9P5apvtF60SbzqlzEdIClRoW6DjjzVBbDpfbbP9olXiTaeY2QhJgQoNhudzAVqfm85X22y/aJV40ylmNkJSoELTAm2wnqrczn21zfaLVok3nWJmIyQFKowfntcFaHVyOl9ts/2iVeJNp5y5CEmBChRoAChQId50ypmLkBSo0LJAW6ynMnpIdjpfbbP9olXiTWcAMxGSAhVGD8/vAnQ2vtpm+0WrxJvOAGYiJAUqNCzQJuuZIDydr7bZftEq8aYzhHkISYEKY4fneQFamZ7OV9tsv2iVeNMZwjyEpECF3AVadcZP56tttl+0SrzpDGIWQlKgQrsCbbOe6vjy/HS+2mb7RavEm84gZiEkBSqMHJ7zBeg8fLXN9otWiTedQcxCSApUaFagjdZTv4DiFaTz1TbbL1ol3nSGMQchKVBh3PC8L0Dn4atttl+0SrzpDGMOQlKgQvYCrbgGTuerbbZftEq86QxkBkJSoEKrAm21nhFLKF1DOl9ts/2iVeJNZyAzEJICFUYNL8AFaEWJp/PVNtsvWiXedIbSv5AUqNCoQJutZ8wiCleRzlfbbL9olXjTGUr/QlKgAgUaAApUiDedofQvJAUqjBleiEfww5eRzlfbbL9olXjTGUz3Qrb4cX/z0cPl8oNPD2/6579cLt/b3vTq0XLNt391srIG0U1pU6Dt1jNqGZ36WkSlkOGMjDedwXQvZIOf962Phzr+3cbQ936++uDFw/4LNMgF6OCFpPO1hFohwxkZbzrD6V3IBj/vT5YPPr17+Xj54PPdLc+X7/3V3eqmtaLPl9/RVzY+ui0UaAAaFGitkOGMjDed4fQu5Pif9xcP11a+erQ5vd/z5vHyZ3frm9b/Pll+qK9sdHRjmhRow/VMsJJ0vhZQLWQ4I+NNp4LOhRz/A/9sez5/ttfy1aPtg6O1qW8e70U+Wdno6MbUDy/MBejQpaTztYBqIcMZGW86FXQu5Pgf+Cebs7v2wGjt66tHD/7+p8vln396+tloujYp0JbrmWAt6XwtoFrIcEbGm04NfQs5+id+fz5/8VCec9qweRC1e8p+q/V6SRu+6IWDk6z3UoItZjijC3SEkP0YGYm+hbQs0M1jqefL5Q8/v/uXj5b7B07d6RpKkVCLGY5lgd4Ssh8jI9G3kC0L9ORlIc83hu6ekjp76j7aA6b6hw+RHsEPe9Ip3SOm29QLGc7IeNOpomsh7a5Anz9872dHHy9PLgii6Vo9vGFPk5vTta+3qRcynJHxplNF10KaFeiz5cnvOs8uCKLp2qBA266nlp59vU29kOGMjDedOnoW0uq38H93quv5U1LRdK0dXrAL0EELSudrAdVChjMy3nTq6FnIFq8D/fDo3xVvnizf357ddy9iPn9VSTRduynQAWf8dL4WUC1kOCPjTaeSjoW0+Euk9R/TfS7/vfZ0762sbHR0Y0YXaOP1VFNe6el8LaBayHBGxptOJR0LOf6H/l7E90/+9PjZ4dPzLx6uXjXy8qfhn7KvHF68C9CefS2gWshwRsabTiUdC9ngh/7lwZvfvHh4f9rfvV3YPatz/bPte9+c/uVHNF1HF2jr9dRTLGw6X0uoFTKckfGmU0u/Qrb4qX/50b2OH6xP52tfny+PfL17uXovxh+enu7D6Vo3vIAXoD37WkSlkOGMjDedWvoVknekF/op0OJVpfPVNtsvWiXedKrpVkgKVBhZoM3XM4JufbXN9otWiTedaroVkgIVaoYX8wK0uNfT+Wqb7RetEm869fQqJAUqjCvQ9usZQ2Gxp/PVNtsvWiXedOrpVUgKVKgYXtQL0NJmT+erbbZftEq86YygUyEpUKHTAr22snS+2mb7RavEm84IOhWSAhVGFajBekbRqa+22X7RKvGmM4JOhaRAheHDi3sBWri2dL7aZvtFq8Sbzhj6FJICFcYUqMV6xtGnr7bZftEq8aYzhj6FpECFwcOLfAFa1u7pfLXN9otWiTedUXQpJAUqdFugl1eXzlfbbL9olXjTGUWXQlKgwogCNVnPWHr01TbbL1ol3nTG0aOQFKgwdHixL0CL1pfOV9tsv2iVeNMZR49CUqBCfYHarGcsPfpqm+0XrRJvOuPoUUgKVBg4vOgXoCUrTOerbbZftEq86YykQyEpUIECDQAFKsSbzkg6FJICFaoL1Gg947kpbDpfbbP9olXiTWcs/QlJgQrDhhf/ArRHX22z/aJV4k1nLP0JSYEKtQVqtZ4GdOerbbZftEq86YymOyEpUKHrAtVXmc5X22y/aJV40xlNd0JSoMKg4WV4BH93s+bT+Wqb7RetEm864+lNSApUqCxQs/W04EbPp/PVNtsvWiXedMbTm5AUqDBkeEkuQLvz1TbbL1ol3nTG05uQFKjQY4HeWGg6X22z/aJV4k2nAZ0JSYEKdQVqt54mdOarbbZftEq86TSgMyEpUGHA8NJcgN6o+nS+2mb7RavEm04L+hKSAhWqCtRwPW242vXpfLXN9otWiTedFvQlJAUqlA8v0QXo9bJP56tttl+0SrzpNKErISlQofsCPV9sOl9ts/2iVeJNpwldCUmBCjUFarmeRnTlq222X7RKvOk0oSshKVCheHipLkCvLjedr7bZftEq8abThp6EpECFigI1XU8revLVNtsvWiXedNrQk5AUqFA6vGQXoNcWnM5X22y/aJV402lER0JSoAIFGgAKVIg3nUZ0JCQFKgwvUNv1tKMfX22z/aJV4k2nFf0ISYEKhcNLdwF6ZcnpfLXN9otWiTedVvQjJAUqDC5Q4/U0pBtfbbP9olXiTacZ3QhJgQplw0t4AXp50el8tc32i1aJN51mdCMkBSpQoAGgQIV402lGN0JSoMLQArVeT0suCJvOV9tsv2iVeNNpRy9CUqBC0fBSXoD246tttl+0SrzptKMXISlQYWCBmq+nKZ34apvtF60SbzoN6URIClQoGV7SC9BLC0/nq222X7RKvOk0pBMhKVCh6wLVz/jpfLXN9otWiTedlvQhJAUqDCtQ+/W0Ra3+dL7aZvtFq8SbTkv6EJICFQqGl/cCtBNfbbP9olXiTaclfQhJgQqDCnSC9TRGEzadr7bZftEq8abTlC6EpECF28NLfAHaia+22X7RKvGm05QuhKRAhc4LVFt9Ol9ts/2iVeJNpy09CEmBCkMKdIr1tKYHX22z/aJV4k2nLT0ISYEKN4eX+wJU6/90vtpm+0WrxJtOYzoQkgIVBhToJOtpzvkJIJ2vttl+0SrxptOYDoSkQIXuC/R8A+l8tc32i1aJN53W5BeSAhVuDS/7I3hlB+l8tc32i1aJN53W5BeSAhXKC3Sa9bQnv6+22X7RKvGm05r8QlKgwo3h5b8APd9DOl9ts/2iVeJNpznphaRABQo0ABSoEG86zUkvJAUqFBfoROuxILuvttl+0SrxptOe7EJSoML14fVwAXq2i3S+2mb7RavEm057sgtJgQqlBTrVekxI7qtttl+0SrzpGJBcSApUuDq8Pi5AT/eRzlfbbL9olXjTMSC5kBSoQIEGgAIV4k3HgORCUqBCYYFOth4bjoRN56tttl+0SrzpWJBbSApUuDa8Xi5As/tqm+0XrRJvOhbkFpICFcoKdLr1GHEobDpfbbP9olXiTceE1EJSoMKV4fVzAZrcV9tsv2iVeNMxIbWQFKgwkwK9y+yrbbZftEq86diQWUjPAv0iDzJi75U0YBF5N64F6r35mZJZSK5Ahctnv64uQA+3k+6Eb5vtF60SbzpGJBaSAhVKCnTK9ZhxeMb3XsspFKgQr02MSCwkBSpc9LWzC9DMvtpm+0WrUKABoECLmU2BRt4QBSrMpkATC0mBCgUFOul6zFjoeC9rDQUqzLJAkwlJgQqXfI020tHo24ixOQpUmFGBFt84ORRoMbcLdNr1mEGBXsj2i1ahQANAgRZzwdfuLkAp0EvZftEqFGgAKNBiKNAAUKACBRoACrSYmwU68XrMoEAvZPtFq1CgAaBAi9F97e8ClAK9lO0XrUKBBoACLeZWgU69HjMo0AvZftEqFGgAKNBiVF87vAClQC9l+0WrUKABoECLoUADQIEKFGgAKNBibhTo5OsxgwK9kO0XrUKBBoACLUbztccLUAr0UrZftAoFGgAKtJjrBTr9esygQC9k+0WrUKABoECLUXzt8gKUAr2U7RetQoEGgAIthgINAAUqUKABoECLuVqgDusxgwK9kO0XrUKBBoACLebc1z4vQCnQS9l+0SoUaAAo0GKuFajHesygQC9k+0WrUKABoECLoUADQIEKFGgAKNBiznzt9BE8BXop2y9ahQINAAVazJUCdVmPGRTohWy/aBUKNAAUaDGnvvZ6AUqBXsr2i1ahQANAgRYznwLV8V7WGgpUmFGBphWSAhUuF6jPeiYg3o8oBSrEm4458bZMgRZzMrxop0IL0vlqm+0XrRJvOubE2zIFWszFAnVazwSk89U22y9aJd50zIm3ZQq0mOPhzeECNJ+vttl+0SrxpmNOvC1ToMVQoAGgQIV40zEn3pYp0GIuFajXeiYgna+22X7RKvGmY068LVOgxRwNbxYXoPl8tc32i1aJNx1z4m2ZAi3mQoG6rWcC0vlqm+0XrRJvOubE2zIFWszh8OZxAZrPV9tsv2iVeNMxJ96WKdBiKNAAUKBCvOmYE2/LFGgxeoH6rWcC0vlqm+0XrRJvOubE2zIFWszB8GZyAZrPV9tsv2iVeNMxJ96WKdBi1AJ1XM8EpPPVNtsvWiXedMyJt2UKtBgZ3lwuQPP5apvtF60SbzrmxNsyBVoMBRoAClSINx1z4m2ZAi1GK1DP9UxAOl9ts/2iVeJNx5x4W6ZAiBEYzwAADmBJREFUi9kPbzYXoPl8tc32i1aJNx1z4m2ZAi1GKVDX9UxAOl9ts/2iVeJNx5x4W6ZAi9kNbz4XoPl8tc32i1aJNx1z4m2ZAi2GAg0ABSrEm4458bZMgRZzXqC+65mAdL7aZvtFq8SbjjnxtkyBFrMd3owuQPP5apvtF60SbzrmxNsyBVrMWYE6r2cC0vlqm+0XrRJvOubE2zIFWsxmeHO6AM3nq222X7RKvOmYE2/LFGgxFGgAKFAh3nTMibdlCrSY0wL1Xs8EpPPVNtsvWiXedMyJt2UKtJj18GZ1AZrPV9tsv2iVeNMxJ96WKdBiTgrUezlTkM5X22y/aJV40zEn3pYp0GJWw5vXBWg+X22z/aJV4k3HnHhbpkCLoUADQIEK8aZjTrwtU6DFHBeo92omIZ2vttl+0SrxpmNOvC1ToMXcD29mF6D5fLXN9otWiTcdc+JtmQIt5qhAvRczDel8tc32i1aJNx1z4m2ZAi2GAg0ABSrEm4458bZMgRbzxRdzewSfz1fbbL9olXjTMSfelinQYg4L1HstE5HOV9tsv2iVeNMxJ96WKdBi5ncBms9X22y/aJV40zEn3pYp0FI2xUmB+kKB7pmTiFsCbpkCLWI3t3n1JwV6nO0XfcaBkM4rmYyYW6ZACziaGAXqCQW64kRIt3VMSNQtU6C3Ob0T4kzPGgr0KNsv+pgZChl2yxToTc7vgzDTs4YCPcr2iz5ihkLG3fIEBfrmo4fL5QefXrxJ+fx6ZQ2iW6DdBVGmZ02fBVopZBQjZyhk4C3bF+irR8sV3/7VhZuUz29WNj66CYGHZ06XBVorZBQjZyhk4C3bF+iT5YNP714+Xj74XL9J+fxmZeOjW6DfA0GmZ02XBVorZBAjLwjZNQPuiKkxL9AXD9en8leP3vu5epPy+e3KRkc3IfLwzOmxQKuFDGIkBXr1jpga8wJ9tvzO9t8P1ZuUz29XNjq6CZGHZ06PBVotZBAjLwnpXXJ2hP4ZNC/QJ8ufrf99vvXy9Cbl89uVjY5uwaU7IMb0rOmxQKuFjGHkRSE7ZuBdMSnWBfrm8faR0IuHu+eUjm5SPv+nW76IwMXheS9spowu0BFChjCSAo31M0iBXif08GYIBXpJyI6J/DM4YYHuXhdydJPy+S0hHjDxED4YLQt0oJAxjKRAb94VkxLwCnS3srHRTQg9PHP6LtCBQsYwkgK9eVdMCgV6g8i/ATSHAj3KHhndBn4Lf/WOmBp+C3+DyMMzp8MCzf5beF4Hev2OmJoJXgf64dG/pzcpn9+ubHR0EyIPz5weC7RayCBGUqBX74ip4S+RbhH473DN6bFAs/8l0hyFDLxl8wJ983j5/smfFh/dpHx+u7LR0W0IPDxzeizQaiGjGDlDIQNv2f7NRF4evLnNi4fr0/rhTccfHK5sfHQb4r4XoTk9Fmi1kGGMnKGQcbc8wfuBvvzoXscP1qfzra+HN518cLCyBtFtCPtu2OZ0WaC1QsYxcoZCht0y70hfQNT/PxZz+izQ6my/6BNmKGTULVOgRSxC/j8CmkOBHmX7RZ8xQyFjbpkCLeXy6yn6hQI9yvaL1pihkAG3TIEWE69NzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtmQItJt7wzIm3ZQpUiDcdc+JtOXCBAihgJITiqjMUKAQDIyEUV50J9n9A4ornz64TM9xyImY4nXRbpkCFdMMbzwy3nIgZTifdlilQId3wxjPDLSdihtNJt2UKVEg3vPHMcMuJmOF00m2ZAhXSDW88M9xyImY4nXRbpkCFdMMbzwy3nIgZTifdlilQId3wxjPDLSdihtNJt2UKFACgEgoUAKASChQAoBIKFACgEgoUAKASChQAoBIKFACgEgr07u7N4+WHhx+/evQdr6VMxdGW//kvl8v3PvjUcTlwBELmEZICvbt78XD54PODj58su/f1cMt/t1zz3s9dVwQCQuYRkgK99/Pb//1gWG+eLPv39WDLz5fv/dXd3cvHy2//yndNsAMh8whJgd4/QHrwT2LoP/902b+vB1u+f+z0s81Nm3/BHYTc3JRCSAr0/oz34atHu7Pds+Xyh//Uva8HW95v/cnx827gBkKub8shJAV69+T+scOT3dnu2ft/fT/N3n092vLuthS+zgGE3NyWQkgK9MXDB5/fK3rwrH33vp5veXXiz/Gkff8g5JokQlKgz1YnuqNpde/r+ZZXt3W+6TQg5Pa2FJuefYG+ebx+yuXw8ULvvipbXv3qM8P5fgYg5JosQs6+QF88XMv5/OBFE737qm354XsZfuU5BxBy/d9ZhJx9gT5b7tgPrHdfz7f8LMnpfg4g5F0mIedeoK8e7Ye3l7RzX8+3/HdpdO0fhLxLJeTcC3Tv5ouH+5l17uvplt88Wb6f4W8+5gFC5hJy7gW6f/HZwbsZdO7r6ZafHL98BFxByFxCzrxA5S8+7p7tx9a3r6dbfpZJ1+5ByGRCzrxAD15s9uLh7kTYt68nW9aecwM3EDKZkPMu0N0bF2z/ezuwrn093fLzZSpfOwchswk57wIFABgBBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkF6s7rHy0WX//MexUAWxByABSoO799d7F46yfeqwDYgpADoEDdebp4+3uLd7xXAbAFIQdAgXpz/4DpnaeLr/3Sex0AaxByCBSoN/cPmH7w5WLxA+91AKxByCFQoN58fH+y/+q7PGsPQUDIIVCgztyr+s7qaSeetYcQIOQgKFBnNg+W7h82fct7JQB3CDkQCtSZj9dP17/+EQ+ZIAQIOQgK1JfNA6bVQyaetYcAIOQwKFBfds813T9k4pV34A9CDoMCdWX/SOn+P3jlHbiDkAOhQF1Z/dXcHp61B28QciAUqCtPD3TlWXtwByEHQoF68tV3D33llXfgDEIOhQL15MuDh0n3/82z9uALQg6FAvXk44OT/P3Jn2ftwReEHAoF6sjxXxx/zCvvwBeEHAwF6siXR7/o/JJn7cEXhBwMBerH6x8dPU1/8iHAxCDkcChQP3777vEZ/imvvANPEHI4FCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCgAQCUUKABAJRQoAEAlFCjMjMUK70VAJ2ASOLPQMQs7+hdgFGgEzugKmoh51JpUKIwHicCZ6Qr09JjID2PBIXBmsgI9PyT2w0hQCJwZXKC/fXfzLOlb3/jF/ravvvtOTc5V/c9yfvP9+w/++BfXvgdmBgUKzlQX6D0/2N328cKuQHc5n2z79Ce3kmA+UKDgTEWBfv2z1b+//2TxtV+ub3n98eJmgQ5/puAk58vFW392d/e7H+1CAShQcKe6QO9e/2hzafib7y0sC3STswv76rty3QuzhwIFZ+oL9P6B+7fu//fpYvHNX1sW6Drnq+9urzw3oQArKFBwZvQV6NO3f3z/+PpGgV463pAr3Q0UKAgUKDhTXaCvP1nsLhFNC/QwZ/UQnt8iwR4KFJwZ8Vv4t/cvKbIp0POc1RMGN3/fD/OBAgVnRhTo6tfiG2wLVHJWv4vnAhT2UKDgTP1D+F+/u39u0vQh/GHOu2/xO3gQKFBwZsRv4b9cyH9Z/hZ+n/OU6084ggIFZ0YU6P6lRcYFusv5hP6EYyhQcCZNgb7+ePH2L2+kwMygQMGZiR7C1/wt/HHOx4evZgJYQYGCM/UF+ut3969pNy3QTc5T+hPOoEDBmTHvxlT+Qnr1kNftP8756rv7j3ghKOygQMGZ+gL9xo/3l4QlBTr0HemPc75cUKBwBgUKzvD/iQR5QSJwhv9XTsgLGsHMMKxnmB2YBABQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUAkFCgBQCQUKAFAJBQoAUMn/B4uhQDVdUYMgAAAAAElFTkSuQmCC" 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<div style="box-sizing: border-box; margin: 0px 0px 10px;">
Mathematically the interaction effect is computed as the cell mean minus the sum of the grand mean, the marginal mean in each condition of one factor minus the grand mean, and the marginal mean in each condition for the other factor minus grand mean (see Maxwell et al., 2018).</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
Let’s consider two cases comparable to the figure above, one where we have a perfect disordinal interaction (the means of 0 and 1 flip around in the other condition, and are 1 and 0) or an ordinal interaction (the effect is present in one condition, with means of 0 and 1, but there is no effect in the other condition, and both means are 0). We can calcuate the interaction effect as follows. First, let’s look at the interaction in a 2x2 matrix:<br />
<br />
<table class="means" style="-webkit-text-stroke-width: 0px; background-color: white; border-collapse: collapse; border-spacing: 0px; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: right; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"><tbody style="box-sizing: border-box;">
<tr style="box-sizing: border-box;"><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;"><br /></td><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;">A1</td><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;">A2</td><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;">marginal</td></tr>
<tr style="box-sizing: border-box;"><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;">B1</td><td class="a1b1" style="background-color: #e74c3c; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">1</td><td class="a2b1" style="background-color: #3498db; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0</td><td class="b1" style="background-color: #a569bd; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0.5</td></tr>
<tr style="box-sizing: border-box;"><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;">B2</td><td class="a1b2" style="background-color: #f1c40f; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0</td><td class="a2b2" style="background-color: #239b56; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">1</td><td class="b2" style="background-color: #66ff00; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0.5</td></tr>
<tr style="box-sizing: border-box;"><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;">marginal</td><td class="a1" style="background-color: orange; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0.5</td><td class="a2" style="background-color: #16a085; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0.5</td><td class="m" style="background-color: #999999; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0.5</td></tr>
</tbody></table>
<br />
The grand mean is (1 + 0 + 0 + 1) / 4 = 0.5.</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
We can compute the marginal means for A1, A2, B1, and B2, which is simply averaging per row and column, which gets us for the A1 column (1+0)/2=0.5. For this perfect disordinal interaction, all marginal means are 0.5. This means there are no main effects. There is no main effect of factor A (because the marginal means for A1 and A2 are both exactly 0.5), nor is there a main effect of B.</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
We can also calculate the interaction effect. For each cell we take the value in the cell (e.g., for a1b1 this is 1) and compute the difference between the cell mean and the additive effect of the two factors as: 1 - (the grand mean of 0.5 + (the marginal mean of a1 minus the grand mean, or 0.5 - 0.5 = 0) + (the marginal mean of b1 minus the grand mean, or 0.5 - 0.5 = 0)).<br />
<br />
Thus, for each cell we get:</div>
<div style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 10px; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
a1b1: <span class="a1b1" style="background-color: #e74c3c; box-sizing: border-box; padding: 0.25em;">1</span> - (<span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.5</span> + (<span class="a1" style="background-color: orange; box-sizing: border-box; padding: 0.25em;">0.5</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.5</span>) + (<span class="b1" style="background-color: #a569bd; box-sizing: border-box; padding: 0.25em;">0.5</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.5</span>)) = 0.5</div>
<div style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 10px; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
a1b2: <span class="a1b2" style="background-color: #f1c40f; box-sizing: border-box; padding: 0.25em;">0</span> - (<span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.5</span> + (<span class="a1" style="background-color: orange; box-sizing: border-box; padding: 0.25em;">0.5</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.5</span>) + (<span class="b2" style="background-color: #66ff00; box-sizing: border-box; padding: 0.25em;">0.5</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.5</span>)) = -0.5</div>
<div style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 10px; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
a2b1: <span class="a2b1" style="background-color: #3498db; box-sizing: border-box; padding: 0.25em;">0</span> - (<span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.5</span> + (<span class="a2" style="background-color: #16a085; box-sizing: border-box; padding: 0.25em;">0.5</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.5</span>) + (<span class="b1" style="background-color: #a569bd; box-sizing: border-box; padding: 0.25em;">0.5</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.5</span>)) = -0.5</div>
<div style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 10px; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
a2b2: <span class="a2b2" style="background-color: #239b56; box-sizing: border-box; padding: 0.25em;">1</span> - (<span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.5</span> + (<span class="a2" style="background-color: #16a085; box-sizing: border-box; padding: 0.25em;">0.5</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.5</span>) + (<span class="b2" style="background-color: #66ff00; box-sizing: border-box; padding: 0.25em;">0.5</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.5</span>)) = 0.5</div>
<br />
<span face=""helvetica neue" , "helvetica" , "arial" , sans-serif" style="background-color: white; color: #333333; display: inline; float: none; font-size: 14px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">Cohen’s </span><span class="math inline" face=""helvetica neue" , "helvetica" , "arial" , sans-serif" style="box-sizing: border-box; color: #333333; font-size: 14px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"><span class="MathJax" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML"><mi>f</mi></math>" id="MathJax-Element-8-Frame" role="presentation" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; direction: ltr; display: inline; float: none; font-size-adjust: none; font-size: 100%; font-style: normal; font-weight: normal; letter-spacing: normal; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: relative; text-align: left; text-indent: 0px; text-transform: none; white-space: nowrap; word-spacing: normal;" tabindex="0"><nobr aria-hidden="true" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; white-space: nowrap;"><span class="math" id="MathJax-Span-107" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0.53em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; font-size: 121%; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0.41em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(1.68em, 1000.41em, 2.92em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -2.53em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-108" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mi" id="MathJax-Span-109" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">f<span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 1px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0.14em;"></span></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 2.53em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: solid; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 1.21em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: -0.32em; width: 0px;"></span></span></nobr><span class="MJX_Assistive_MathML" role="presentation" style="-ms-user-select: none; border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; clip: rect(1px, 1px, 1px, 1px); display: inline; height: 1px; left: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: static; text-decoration: none; top: 0px; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; transition: none 0s ease 0s; vertical-align: 0px; width: 1px;"><math xmlns="http://www.w3.org/1998/Math/MathML"><mi> </mi></math></span></span></span><span face=""helvetica neue" , "helvetica" , "arial" , sans-serif" style="background-color: white; color: #333333; display: inline; float: none; font-size: 14px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;">is then </span><span class="math inline" face=""helvetica neue" , "helvetica" , "arial" , sans-serif" style="box-sizing: border-box; color: #333333; font-size: 14px; font-style: normal; font-variant: normal; font-weight: 400; letter-spacing: normal; text-align: left; text-decoration: none; text-indent: 0px; text-transform: none; white-space: normal; word-spacing: 0px;"><span class="MathJax" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML"><mi>f</mi><mo>=</mo><mfrac><msqrt><mfrac><mrow><msup><mn>0.5</mn><mn>2</mn></msup><mo>+</mo><mo>−</mo><msup><mn>0.5</mn><mn>2</mn></msup><mo>+</mo><mo>−</mo><msup><mn>0.5</mn><mn>2</mn></msup><mo>+</mo><msup><mn>0.5</mn><mn>2</mn></msup></mrow><mn>4</mn></mfrac></msqrt><mn>2</mn></mfrac><mo>=</mo><mn>0.25</mn></math>" id="MathJax-Element-9-Frame" role="presentation" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; border: 0px none; box-sizing: border-box; direction: ltr; display: inline; float: none; font-size-adjust: none; font-size: 100%; font-style: normal; font-weight: normal; letter-spacing: normal; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; padding: 0px; position: relative; text-align: left; text-indent: 0px; text-transform: none; white-space: nowrap; word-spacing: normal;" tabindex="0"><nobr aria-hidden="true" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; white-space: nowrap;"><span class="math" id="MathJax-Span-110" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 14.11em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; font-size: 121%; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 11.62em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(0.2em, 1011.56em, 3.11em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -2.53em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-111" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mi" id="MathJax-Span-112" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">f<span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 1px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0.14em;"></span></span><span class="mo" id="MathJax-Span-113" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0.31em; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">=</span><span class="mfrac" id="MathJax-Span-114" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0.31em; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0.12em; margin-right: 0.12em; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 6.63em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(2.69em, 1006.51em, 4.78em, -1000em); display: inline; left: 50%; line-height: normal; margin-bottom: 0px; margin-left: -3.25em; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -5.03em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="msqrt" id="MathJax-Span-115" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 6.51em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(2.93em, 1005.72em, 4.52em, -1000em); display: inline; left: 0.79em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -4.01em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-116" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mfrac" id="MathJax-Span-117" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0.12em; margin-right: 0.12em; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 5.48em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(3.34em, 1005.36em, 4.26em, -1000em); display: inline; left: 50%; line-height: normal; margin-bottom: 0px; margin-left: -2.68em; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -4.43em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-118" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="msubsup" id="MathJax-Span-119" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0.91em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(3.49em, 1000.59em, 4.19em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -4.01em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mn" id="MathJax-Span-120" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">0.5</span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; left: 0.62em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -4.16em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mn" id="MathJax-Span-121" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">2</span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-122" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">+</span><span class="mo" id="MathJax-Span-123" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">−</span><span class="msubsup" id="MathJax-Span-124" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0.91em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(3.49em, 1000.59em, 4.19em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -4.01em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mn" id="MathJax-Span-125" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">0.5</span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; left: 0.62em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -4.16em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mn" id="MathJax-Span-126" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">2</span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-127" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">+</span><span class="mo" id="MathJax-Span-128" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">−</span><span class="msubsup" id="MathJax-Span-129" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0.91em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(3.49em, 1000.59em, 4.19em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -4.01em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mn" id="MathJax-Span-130" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">0.5</span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; left: 0.62em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -4.16em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mn" id="MathJax-Span-131" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">2</span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-132" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">+</span><span class="msubsup" id="MathJax-Span-133" style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: relative; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0.91em;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(3.49em, 1000.59em, 4.19em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -4.01em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mn" id="MathJax-Span-134" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">0.5</span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; left: 0.62em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -4.16em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mn" id="MathJax-Span-135" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">2</span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span></span></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(3.49em, 1000.23em, 4.19em, -1000em); display: inline; left: 50%; line-height: normal; margin-bottom: 0px; margin-left: -0.12em; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -3.68em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mn" id="MathJax-Span-136" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">4</span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(0.81em, 1005.48em, 1.24em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -1.2em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: solid; border-top-width: 1.3px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0em; width: 5.48em;"></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 1.06em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span></span></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(0.92em, 1005.72em, 1.31em, -1000em); display: inline; left: 0.79em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -2.24em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: solid; border-top-width: 1.3px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: -0.07em; width: 5.72em;"></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 1.06em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(2.38em, 1000.81em, 4.47em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -3.7em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixsizetwosym"; font-size: 70.7%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">√</span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(3.35em, 1000.33em, 4.19em, -1000em); display: inline; left: 50%; line-height: normal; margin-bottom: 0px; margin-left: -0.17em; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -3.61em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span class="mn" id="MathJax-Span-137" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 70.7%; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">2</span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; clip: rect(0.81em, 1006.63em, 1.24em, -1000em); display: inline; left: 0em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: absolute; text-decoration: none; top: -1.28em; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px;"><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: solid; border-top-width: 1.3px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0em; width: 6.63em;"></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 1.06em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-138" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0.31em; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">=</span><span class="mn" id="MathJax-Span-139" style="border-bottom-color: currentcolor; border-bottom-style: none; border-bottom-width: 0px; border-left-color: currentcolor; border-left-style: none; border-left-width: 0px; border-right-color: currentcolor; border-right-style: none; border-right-width: 0px; border-top-color: currentcolor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0.31em; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-timing-function: ease; vertical-align: 0px;">0.25</span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: none; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 2.53em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: 0px; width: 0px;"></span></span></span><span style="border-bottom-color: currentColor; border-bottom-style: none; border-bottom-width: 0px; border-image-outset: 0; border-image-repeat: stretch; border-image-slice: 100%; border-image-source: none; border-image-width: 1; border-left-color: currentColor; border-left-style: solid; border-left-width: 0px; border-right-color: currentColor; border-right-style: none; border-right-width: 0px; border-top-color: currentColor; border-top-style: none; border-top-width: 0px; box-sizing: border-box; display: inline-block; height: 3.23em; line-height: normal; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; margin-top: 0px; overflow: hidden; padding-bottom: 0px; padding-left: 0px; padding-right: 0px; padding-top: 0px; position: static; text-decoration: none; transition-delay: 0s; transition-duration: 0s; transition-property: none; transition-timing-function: ease; vertical-align: -0.55em; width: 0px;"></span></span></nobr></span></span><span class="math inline" style="box-sizing: border-box;"><span class="MathJax" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML"><mi>f</mi><mo>=</mo><mfrac><msqrt><mfrac><mrow><msup><mn>0.5</mn><mn>2</mn></msup><mo>+</mo><mo>−</mo><msup><mn>0.5</mn><mn>2</mn></msup><mo>+</mo><mo>−</mo><msup><mn>0.5</mn><mn>2</mn></msup><mo>+</mo><msup><mn>0.5</mn><mn>2</mn></msup></mrow><mn>4</mn></mfrac></msqrt><mn>2</mn></mfrac><mo>=</mo><mn>0.25</mn></math>" id="MathJax-Element-9-Frame" role="presentation" style="border: 0px none; box-sizing: border-box; direction: ltr; display: inline; float: none; font-size: 14px; font-style: normal; font-weight: normal; letter-spacing: normal; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding: 0px; position: relative; text-align: left; text-indent: 0px; text-transform: none; white-space: nowrap; word-spacing: normal;" tabindex="0"><span class="MJX_Assistive_MathML" role="presentation" style="border: 0px none; box-sizing: border-box; clip: rect(1px, 1px, 1px, 1px); display: inline; height: 1px; left: 0px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; text-decoration: none; top: 0px; transition: none 0s ease 0s; user-select: none; vertical-align: 0px; width: 1px;"><math xmlns="http://www.w3.org/1998/Math/MathML"><mn></mn></math></span></span></span><br />
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
or in R code: <code style="background-color: rgba(0, 0, 0, 0.04); border-radius: 4px; box-sizing: border-box; color: inherit; font-family: monospace; font-size: 12.6px; padding: 2px 4px; white-space: pre;">sqrt(((0.5)^2 +(-0.5)^2 + (-0.5)^2 + (0.5)^2)/4)/2 = 0.25</code>.</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
For the ordinal interaction the grand mean is (1 + 0 + 0 + 0) / 4, or 0.25. The marginal means are a1: 0.5, a2: 0, b1: 0.5, and b2: 0.<br />
<br />
<br />
<table class="means" style="-webkit-text-stroke-width: 0px; background-color: white; border-collapse: collapse; border-spacing: 0px; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: right; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;"><tbody style="box-sizing: border-box;">
<tr style="box-sizing: border-box;"><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;"><br /></td><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;">A1</td><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;">A2</td><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;">marginal</td></tr>
<tr style="box-sizing: border-box;"><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;">B1</td><td class="a1b1" style="background-color: #e74c3c; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">1</td><td class="a2b1" style="background-color: #3498db; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0</td><td class="b1" style="background-color: #a569bd; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0.5</td></tr>
<tr style="box-sizing: border-box;"><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;">B2</td><td class="a1b2" style="background-color: #f1c40f; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0</td><td class="a2b2" style="background-color: #239b56; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0</td><td class="b2" style="background-color: #66ff00; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0</td></tr>
<tr style="box-sizing: border-box;"><td style="border: 1px solid black; box-sizing: border-box; padding: 0.5em;">marginal</td><td class="a1" style="background-color: orange; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0.5</td><td class="a2" style="background-color: #16a085; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0</td><td class="m" style="background-color: #999999; border: 1px solid black; box-sizing: border-box; padding: 0.5em;">0.25</td></tr>
</tbody></table>
<br /></div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
Completing the calculation for all four cells for the ordinal interaction gives:<br />
<br />
<div style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 10px; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
a1b1: <span class="a1b1" style="background-color: #e74c3c; box-sizing: border-box; padding: 0.25em;">1</span> - (<span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.25</span> + (<span class="a1" style="background-color: orange; box-sizing: border-box; padding: 0.25em;">0.5</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.25</span>) + (<span class="b1" style="background-color: #a569bd; box-sizing: border-box; padding: 0.25em;">0.5</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.25</span>)) = 0.25</div>
<div style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 10px; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
a1b2: <span class="a1b2" style="background-color: #f1c40f; box-sizing: border-box; padding: 0.25em;">0</span> - (<span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.25</span> + (<span class="a1" style="background-color: orange; box-sizing: border-box; padding: 0.25em;">0.5</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.25</span>) + (<span class="b2" style="background-color: #66ff00; box-sizing: border-box; padding: 0.25em;">0</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.25</span>)) = -0.25</div>
<div style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 10px; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
a2b1: <span class="a2b1" style="background-color: #3498db; box-sizing: border-box; padding: 0.25em;">0</span> - (<span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.25</span> + (<span class="a2" style="background-color: #16a085; box-sizing: border-box; padding: 0.25em;">0</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.25</span>) + (<span class="b1" style="background-color: #a569bd; box-sizing: border-box; padding: 0.25em;">0.5</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.25</span>)) = -0.25</div>
<div style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; margin: 0px 0px 10px; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
a2b2: <span class="a2b2" style="background-color: #239b56; box-sizing: border-box; padding: 0.25em;">0</span> - (<span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.25</span> + (<span class="a2" style="background-color: #16a085; box-sizing: border-box; padding: 0.25em;">0</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.25</span>) + (<span class="b2" style="background-color: #66ff00; box-sizing: border-box; padding: 0.25em;">0</span> - <span class="m" style="background-color: #999999; box-sizing: border-box; padding: 0.25em;">0.25</span>)) = 0.25</div>
</div>
<br />
<span face=""helvetica neue" , "helvetica" , "arial" , sans-serif" style="font-variant-east-asian: normal; font-variant-numeric: normal;">Cohen’s </span><span class="math inline" style="box-sizing: border-box; font-variant-east-asian: normal; font-variant-numeric: normal;"><span class="MathJax" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML"><mi>f</mi></math>" id="MathJax-Element-10-Frame" role="presentation" style="border: 0px none; box-sizing: border-box; direction: ltr; display: inline; float: none; font-size: 100%; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding: 0px; position: relative; white-space: nowrap; word-spacing: normal;" tabindex="0"><nobr aria-hidden="true" style="border: 0px none currentcolor; box-sizing: border-box; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; padding: 0px; transition: none 0s ease 0s; vertical-align: 0px;"><span class="math" id="MathJax-Span-140" style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0.53em;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; font-size: 121%; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 0.41em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(1.68em, 1000.41em, 2.92em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -2.53em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-141" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-142" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">f<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0.14em;"></span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 2.53em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span><span style="border-color: currentcolor; border-image: none 100% / 1 / 0 stretch; border-style: none none none solid; border-width: 0px; box-sizing: border-box; display: inline-block; height: 1.21em; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: -0.32em; width: 0px;"></span></span></nobr></span></span><span face=""helvetica neue" , "helvetica" , "arial" , sans-serif" style="font-variant-east-asian: normal; font-variant-numeric: normal;"> is then </span><span class="math inline" style="box-sizing: border-box; font-variant-east-asian: normal; font-variant-numeric: normal;"><span class="MathJax" data-mathml="<math xmlns="http://www.w3.org/1998/Math/MathML"><mi>f</mi><mo>=</mo><mfrac><msqrt><mfrac><mrow><msup><mn>0.25</mn><mn>2</mn></msup><mo>+</mo><mo>−</mo><msup><mn>0.25</mn><mn>2</mn></msup><mo>+</mo><mo>−</mo><msup><mn>0.25</mn><mn>2</mn></msup><mo>+</mo><msup><mn>0.25</mn><mn>2</mn></msup></mrow><mn>4</mn></mfrac></msqrt><mn>2</mn></mfrac><mo>=</mo><mn>0.125</mn></math>" id="MathJax-Element-11-Frame" role="presentation" style="border: 0px none; box-sizing: border-box; direction: ltr; display: inline; float: none; font-size: 100%; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; overflow-wrap: normal; padding: 0px; position: relative; white-space: nowrap; word-spacing: normal;" tabindex="0"><nobr aria-hidden="true" style="border: 0px none currentcolor; box-sizing: border-box; line-height: normal; margin: 0px; max-height: none; max-width: none; min-height: 0px; min-width: 0px; padding: 0px; transition: none 0s ease 0s; vertical-align: 0px;"><span class="math" id="MathJax-Span-143" style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 15.94em;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; font-size: 121%; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 13.16em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(0.2em, 1013.1em, 3.11em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -2.53em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-144" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mi" id="MathJax-Span-145" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-style: italic; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">f<span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0.14em;"></span></span><span class="mo" id="MathJax-Span-146" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.31em; position: static; transition: none 0s ease 0s; vertical-align: 0px;">=</span><span class="mfrac" id="MathJax-Span-147" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.31em; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px 0.12em; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 7.63em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(2.69em, 1007.51em, 4.78em, -1000em); left: 50%; line-height: normal; margin: 0px 0px 0px -3.75em; padding: 0px; position: absolute; top: -5.03em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="msqrt" id="MathJax-Span-148" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 7.51em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(2.93em, 1006.72em, 4.52em, -1000em); left: 0.79em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-149" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mfrac" id="MathJax-Span-150" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px 0.12em; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 6.48em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.34em, 1006.36em, 4.26em, -1000em); left: 50%; line-height: normal; margin: 0px 0px 0px -3.18em; padding: 0px; position: absolute; top: -4.43em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mrow" id="MathJax-Span-151" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span class="msubsup" id="MathJax-Span-152" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 1.16em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.49em, 1000.84em, 4.19em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-153" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">0.25</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; left: 0.87em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.16em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-154" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">2</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-155" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">+</span><span class="mo" id="MathJax-Span-156" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">−</span><span class="msubsup" id="MathJax-Span-157" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 1.16em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.49em, 1000.84em, 4.19em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-158" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">0.25</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; left: 0.87em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.16em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-159" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">2</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-160" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">+</span><span class="mo" id="MathJax-Span-161" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">−</span><span class="msubsup" id="MathJax-Span-162" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 1.16em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.49em, 1000.84em, 4.19em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-163" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">0.25</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; left: 0.87em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.16em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-164" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">2</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-165" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">+</span><span class="msubsup" id="MathJax-Span-166" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; padding: 0px; position: relative; transition: none 0s ease 0s; vertical-align: 0px; width: 1.16em;"><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.49em, 1000.84em, 4.19em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.01em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-167" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">0.25</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; left: 0.87em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -4.16em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-168" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">2</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.49em, 1000.23em, 4.19em, -1000em); left: 50%; line-height: normal; margin: 0px 0px 0px -0.12em; padding: 0px; position: absolute; top: -3.68em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-169" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 50%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">4</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(0.81em, 1006.48em, 1.24em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -1.2em; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border-color: currentcolor; border-image: none 100% / 1 / 0 stretch; border-style: solid none none; border-width: 1.3px 0px 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0em; width: 6.48em;"></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1.06em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(0.92em, 1006.72em, 1.31em, -1000em); left: 0.79em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -2.24em; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border-color: currentcolor; border-image: none 100% / 1 / 0 stretch; border-style: solid none none; border-width: 1.3px 0px 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: -0.07em; width: 6.72em;"></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1.06em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(2.38em, 1000.81em, 4.47em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -3.7em; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border: 0px none currentcolor; box-sizing: border-box; font-family: "stixsizetwosym"; font-size: 70.7%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">√</span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(3.35em, 1000.33em, 4.19em, -1000em); left: 50%; line-height: normal; margin: 0px 0px 0px -0.17em; padding: 0px; position: absolute; top: -3.61em; transition: none 0s ease 0s; vertical-align: 0px;"><span class="mn" id="MathJax-Span-170" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; font-size: 70.7%; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px;">2</span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 4.01em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span><span style="border: 0px none currentcolor; box-sizing: border-box; clip: rect(0.81em, 1007.63em, 1.24em, -1000em); left: 0em; line-height: normal; margin: 0px; padding: 0px; position: absolute; top: -1.28em; transition: none 0s ease 0s; vertical-align: 0px;"><span style="border-color: currentcolor; border-image: none 100% / 1 / 0 stretch; border-style: solid none none; border-width: 1.3px 0px 0px; box-sizing: border-box; display: inline-block; height: 0px; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0em; width: 7.63em;"></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 1.06em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span></span><span class="mo" id="MathJax-Span-171" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.31em; position: static; transition: none 0s ease 0s; vertical-align: 0px;">=</span><span class="mn" id="MathJax-Span-172" style="border: 0px none currentcolor; box-sizing: border-box; display: inline; font-family: "stixgeneral"; line-height: normal; margin: 0px; padding: 0px 0px 0px 0.31em; position: static; transition: none 0s ease 0s; vertical-align: 0px;">0.125</span></span><span style="border: 0px none currentcolor; box-sizing: border-box; display: inline-block; height: 2.53em; line-height: normal; margin: 0px; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: 0px; width: 0px;"></span></span></span><span style="border-color: currentcolor; border-image: none 100% / 1 / 0 stretch; border-style: none none none solid; border-width: 0px; box-sizing: border-box; display: inline-block; height: 3.23em; line-height: normal; margin: 0px; overflow: hidden; padding: 0px; position: static; transition: none 0s ease 0s; vertical-align: -0.55em; width: 0px;"></span></span></nobr></span></span><br />
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
<br />
or in R code: <code style="background-color: rgba(0, 0, 0, 0.04); border-radius: 4px; box-sizing: border-box; color: inherit; font-family: monospace; font-size: 12.6px; padding: 2px 4px; white-space: pre;">sqrt(((0.25)^2 +(-0.25)^2 + (-0.25)^2 + (0.25)^2)/4)/2 = 0.125</code>.</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
<br />
We see the effect size of the cross-over interaction (<i style="box-sizing: border-box;">f</i> = 0.25) is twice as large as the effect size of the ordinal interaction (<i style="box-sizing: border-box;">f</i> = 0.125).</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
If the math so far was a bit too much to follow, there is an easier way to think of why the effect sizes are halved. In the disordinal interaction we are comparing cells a1b1 and a2b2 against a1b2 and a2b1, or (1+1)/2 vs. (0+0)/2. Thus, if we see this as a <i style="box-sizing: border-box;">t</i>-test for a contrast, it is clear the mean difference is 1, as it was in the simple effect we started with. For the ordinal interaction, we have (1+0)/2 vs. (0+0)/2, so the mean difference is halved, namely 0.5.</div>
</div>
<div class="section level1" id="power-for-interactions" style="-webkit-text-stroke-width: 0px; background-color: white; box-sizing: border-box; color: #333333; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 14px; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
<h1 style="box-sizing: border-box; color: inherit; font-family: inherit; font-size: 34px; font-weight: 500; line-height: 1.1; margin: 20px 0px 10px;">
Power for interactions</h1>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
All of the above obviously matters for the statistical power we will have when we examine interaction effects in our experiments. Let’s use Superpower to perform power analyses for the disordinal interaction first, if we would collect 50 participants in each condition.</div>
<pre class="r" style="background-color: whitesmoke; border-radius: 4px; border: 1px solid rgb(204, 204, 204); box-sizing: border-box; color: #333333; display: block; font-family: monospace; font-size: 13px; line-height: 1.42857; margin: 0px 0px 10px; overflow-wrap: break-word; overflow: auto; padding: 9.5px; word-break: break-all;"><code class="hljs" style="background-color: transparent; border-radius: 0px; box-sizing: border-box; color: inherit; font-family: monospace; font-size: inherit; padding: 0px; white-space: pre-wrap;">design ← ANOVA_design(
design = <span class="hljs-string" style="box-sizing: border-box; color: #dd1144;">"2b*2b"</span>,
n = <span class="hljs-number" style="box-sizing: border-box; color: #009999;">50</span>,
mu = c(<span class="hljs-number" style="box-sizing: border-box; color: #009999;">1</span>, <span class="hljs-number" style="box-sizing: border-box; color: #009999;">0</span>, <span class="hljs-number" style="box-sizing: border-box; color: #009999;">0</span>, <span class="hljs-number" style="box-sizing: border-box; color: #009999;">1</span>),
sd = <span class="hljs-number" style="box-sizing: border-box; color: #009999;">2</span>)
ANOVA_exact(design, alpha_level = <span class="hljs-number" style="box-sizing: border-box; color: #009999;">0.03</span>)</code></pre>
<pre style="background-color: white; border-radius: 4px; border: 1px solid rgb(204, 204, 204); box-sizing: border-box; color: #333333; display: block; font-family: monospace; font-size: 13px; line-height: 1.42857; margin: 0px 0px 10px; overflow-wrap: break-word; overflow: auto; padding: 9.5px; word-break: break-all;"><code class="hljs" style="background-color: transparent; border-radius: 0px; box-sizing: border-box; color: inherit; font-family: monospace; font-size: inherit; padding: 0px; white-space: pre-wrap;">## Power and Effect sizes for ANOVA tests
## power partial_eta_squared cohen_f non_centrality
## a 3.000 0.00 0.0000 0.0
## b 3.000 0.00 0.0000 0.0
## a:b 91.055 0.06 0.2525 12.5
##
## Power and Effect sizes for pairwise comparisons (t-tests)
## power effect_size
## p_a_a1_b_b1_a_a1_b_b2 61.78 -0.5
## p_a_a1_b_b1_a_a2_b_b1 61.78 -0.5
## p_a_a1_b_b1_a_a2_b_b2 3.00 0.0
## p_a_a1_b_b2_a_a2_b_b1 3.00 0.0
## p_a_a1_b_b2_a_a2_b_b2 61.78 0.5
## p_a_a2_b_b1_a_a2_b_b2 61.78 0.5</code></pre>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
First let’s look at the Power and Effect size for the pairwise comparisons. Not surprisingly, these are just the same as our original t-test, given that we have 50 observations per condition, and our mean difference is either 1, or a Cohen’s d of 0.5 (in which case we have 61.78% power) or the mean difference is 0, and we have no power (because there is no true effect) but we wil observe significant results 3% of the time because we set our apha level to 0.03.</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
Then, let’s look at the results for the ANOVA. Since there are no main effects in a perfect crossover interaction, we have a 3% Type 1 error rate. We see the power for the crossover interaction between factor a and b is 91.06%. This is much larger than the power for the simple effects. The reason is that the contrast that is equal to the test of the interaction is based on all 200 observations. Unlike the pairwise comparisons with 50 vs 50 observations, the contrast for the interaction has 100 vs 100 observations. Given that the effect size is the same (<i style="box-sizing: border-box;">f</i> = 0.25) we end up with much higher power.</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
If you current think it is impossible to find a statistically significant interaction without a huge sample size, you clearly see this is wrong. Power <i style="box-sizing: border-box;">can</i> be higher for an interaction than for the simpe effect - but this depends on the pattern of means underlying the interaction. If possible, design studies where your theory predicts a perfect crossover interaction.</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
For the ordinal interaction, our statistical power does not look that good based on an a-priori power analysis. Superpower tells us we have 33.99% power for the main effects and interaction (yes, we have exactly the same power for the 2 main effects and the interaction effect - if you think about the three contrasts that are tested, you will see these have the same effect size).<br />
<br />
<pre class="r" style="background-color: whitesmoke; border-radius: 4px; border: 1px solid rgb(204, 204, 204); box-sizing: border-box; color: #333333; display: block; font-family: monospace; font-size: 13px; line-height: 1.42857; margin: 0px 0px 10px; overflow-wrap: break-word; overflow: auto; padding: 9.5px; word-break: break-all;"><code class="hljs" style="background-color: transparent; border-radius: 0px; box-sizing: border-box; color: inherit; font-family: monospace; font-size: inherit; padding: 0px; white-space: pre-wrap;">design ← ANOVA_design(
design = <span class="hljs-string" style="box-sizing: border-box; color: #dd1144;">"2b*2b"</span>,
n = <span class="hljs-number" style="box-sizing: border-box; color: #009999;">50</span>,
mu = c(<span class="hljs-number" style="box-sizing: border-box; color: #009999;">1</span>, <span class="hljs-number" style="box-sizing: border-box; color: #009999;">0</span>, <span class="hljs-number" style="box-sizing: border-box; color: #009999;">0</span>, <span style="color: #009999;">0</span>),
sd = <span class="hljs-number" style="box-sizing: border-box; color: #009999;">2</span>)
ANOVA_exact(design, alpha_level = <span class="hljs-number" style="box-sizing: border-box; color: #009999;">0.03</span>)</code></pre>
</div>
</div>
<pre style="background-color: white; border-radius: 4px; border: 1px solid rgb(204, 204, 204); box-sizing: border-box; color: #333333; display: block; font-family: monospace; font-size: 13px; line-height: 1.42857; margin: 0px 0px 10px; overflow-wrap: break-word; overflow: auto; padding: 9.5px; word-break: break-all;"><code class="hljs" style="background-color: transparent; border-radius: 0px; box-sizing: border-box; color: inherit; font-family: monospace; font-size: inherit; padding: 0px; white-space: pre-wrap;">## Power and Effect sizes for ANOVA tests
## power partial_eta_squared cohen_f non_centrality
## a 33.9869 0.0157 0.1263 3.125
## b 33.9869 0.0157 0.1263 3.125
## a:b 33.9869 0.0157 0.1263 3.125
##
## Power and Effect sizes for pairwise comparisons (t-tests)
## power effect_size
## p_a_a1_b_b1_a_a1_b_b2 61.78 -0.5
## p_a_a1_b_b1_a_a2_b_b1 61.78 -0.5
## p_a_a1_b_b1_a_a2_b_b2 61.78 -0.5
## p_a_a1_b_b2_a_a2_b_b1 3.00 0.0
## p_a_a1_b_b2_a_a2_b_b2 3.00 0.0
## p_a_a2_b_b1_a_a2_b_b2 3.00 0.0</code></pre>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
If you have heard people say you should be careful when designing studies predicting interaction patterns because you might have very low power, this is the type of pattern of means they are warning about. Maxwell, Delaney, and Kelley (2018) discuss why power for interactions is often smaller, and note interactions effects are often smaller in the real world, and we often examine ordinal interactions. This might be true. But in experimental psychology it might be possile to think about hypotheses that predict disordinal interactions. In addition to the fact that such predictions are often theoretically riskier and more impressive (after all, many things can make an effect go away, but without your theory it might be difficult to explain why an effect flips around) they also have larger effects and are easier to test with high power.</div>
<div style="box-sizing: border-box; margin: 0px 0px 10px;">
Some years ago other blog posts by <a href="http://datacolada.org/17" style="background-color: transparent; box-sizing: border-box; color: #337ab7; text-decoration: none;">Uri Simonsohn</a> and <a href="https://approachingblog.wordpress.com/2018/01/24/powering-your-interaction-2/" style="background-color: transparent; box-sizing: border-box; color: #337ab7; text-decoration: none;">Roger Giner-Sorolla</a> did a great job in warning researchers they need large sample sizes for ordinal interactions, and my post repeats this warning. But it would be a shame if researchers would stop examining interaction effects. There are some nice benefits studying interactions, such as 1) making riskier theoretical predictions, 2) greater generalizability (if there is no interaction effect, you might show a main effect operates across different conditions of a second factor) and 3) if you want to study two main effects it is more efficient to do this in a 2x2 design than in two seperate designs (see Maxwell, Delaney, & Kelley, 2018 for a discussion). So maybe this blog post has been able to highlight some scenarios where examining interaction effects is still beneficial.<br />
<br />
<i>Thanks to Lisa DeBruine for the idea and html-code to color code the calculation of the interaction effect.</i><br />
<br />
<i> </i> </div>
<script src="https://gist.github.com/Lakens/9c9bdcfad09fcac5d530ad9e209a169b.js"></script>Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com2tag:blogger.com,1999:blog-987850932434001559.post-76626846744652728192020-03-12T11:29:00.003+01:002020-03-12T11:48:54.866+01:00What’s a family in family-wise error control?<span lang="EN-US" style="mso-ansi-language: EN-US;">When you perform multiple comparisons in a study, you need to control your alpha level for multiple comparisons. It is generally recommended to control for the family-wise error rate, but there is some confusion about what a 'family' is. As Bretz, Hothorn, & Westfall </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(2011)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> write in their excellent book “Multiple Comparisons Using R” on page 15: “The appropriate choice of null hypotheses being of primary interest is a controversial question. That is, it is not always clear which set of hypotheses should constitute the family <i>H<sub>1</sub></i>,…,<i>H<sub>m</sub></i>. This topic has often been in dispute and there is no general consensus.” In one of the best papers on controlling for multiple comparisons out there, Bender & Lange </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(2001)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> write: “Unfortunately, there is no simple and unique answer to when it is appropriate to control which error rate. Different persons may have different but nevertheless reasonable opinions. In addition to the problem of deciding which error rate should be under control, it has to be defined first which tests of a study belong to one experiment.” The <a href="https://en.wikipedia.org/wiki/Family-wise_error_rate" target="_blank">Wikipedia page on family-wise error rate</a> is a mess. </span><br />
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<span lang="EN-US" style="mso-ansi-language: EN-US;">I will be honest: I have never understood this confusion about what a family of tests is when controlling the family-wise error rate. At least not in a Neyman-Pearson approach to hypothesis testing, where the goal is to use data to make decisions about how to act. Neyman </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Neyman, 1957)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> calls his approach <i>inductive behavior</i>. The outcome of an experiment leads one to take different possible actions, which can be either practical (e.g., implement a new procedure, abandon a research line) or scientific (e.g., claim there is or is no effect). From an error-statistical approach </span><span lang="EN-US" style="mso-ansi-language: EN-US; mso-bidi-font-family: Arial;">(Mayo, 2018)</span><span lang="EN-US" style="mso-ansi-language: EN-US;"> inflated Type 1 error rates mean that it has become very likely that you will be able to claim support for your hypothesis, even when the hypothesis is wrong. This reduces the severity of the test. To prevent this, we need to control our error rate <i>at the level of our claim</i>.</span></div>
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<span lang="EN-US" style="mso-ansi-language: EN-US;">One reason the issue of family-wise error rates might remain vague, is that researchers are often vague about their claims. We do not specify our hypotheses unambiguously, and therefore this issue remains unclear. To be honest, I suspect another reason there is a continuing debate about whether and how to lower the alpha level to control for multiple comparisons in some disciplines is that 1) there are a surprisingly large number of papers written on this topic that argue you do not need to control for multiple comparisons, which are 2) cited a huge number of times giving rise to the feeling that surely they must have a point. Regrettably, the main reason these papers are written is because there are people who don't think a Neyman-Pearson approach to hypothesis testing is a good idea, and the main reason these papers are cited is because doing so is convenient for researchers who want to publish statistically significant results, as they can justify why they are not lowering their alpha level, making that <i>p</i> = 0.02 in one of three tests really 'significant'. <b>All papers that argue against the need to control for multiple comparisons when testing hypotheses are wrong.</b> Yes, their existence and massive citation counts frustrate me. </span><span lang="EN-US" style="mso-ansi-language: EN-US;"><span lang="EN-US" style="mso-ansi-language: EN-US;">It is fine not to test a hypothesis, but when you do, and you make a claim based on a test, you need to control your error rates. </span></span><br />
<br />
<span lang="EN-US" style="mso-ansi-language: EN-US;">But let’s get back to our first problem, which we can solve by making the claims people need to control Type 1 error rates for less vague. Lisa DeBruine and I recently proposed <a href="https://psyarxiv.com/5xcda/" target="_blank">machine readable hypothesis tests</a> to remove any ambiguity in the tests we will perform to examine statistical predictions, and when we will consider a claim corroborated or falsified. In this post, I am going to use our R package ‘<a href="https://scienceverse.github.io/scienceverse/">scienceverse</a>’ to clarify what constitutes a family of tests when controlling the family-wise error rate. </span></div>
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<h3 class="MsoNormal">
<b><span lang="EN-US" style="mso-ansi-language: EN-US;">An example of formalizing family-wise error control</span></b></h3>
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<br /></div>
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<span lang="EN-US" style="mso-ansi-language: EN-US;">Let’s assume we collect data from 100 participants in a control and treatment condition. We collect 3 dependent variables (dv1, dv2, and dv3). In the population there is no difference between groups on any of these three variables (the true effect size is 0). We will analyze the three dv’s in independent <i>t</i>-tests. This requires specifying our alpha level, and thus deciding whether we need to correct for multiple comparisons. How we control error rates depends on claim we want to make. </span></div>
<div class="MsoNormal">
<br />
<span lang="EN-US" style="mso-ansi-language: EN-US;">We might want to act as if (or claim that) our treatment works if there is a difference between the treatment and control conditions on any of the three variables. In scienceverse terms, this means we consider the prediction corroborated when the <i>p</i>-value of the first <i>t</i>-test is smaller than alpha level, the <i>p</i>-value of the second <i>t</i>-test is smaller than the alpha level, or the <i>p</i>-value of the first <i>t</i>-test is smaller than the alpha level. In the scienceverse code, we specify a criterion for each test (a p-value smaller than the alpha level, <i>p.value < alpha_level</i>) and conclude the hypothesis is corroborated if either of these criteria are met (<i>"p_t_1 | p_t_2 | p_t_3"</i>). <span style="mso-spacerun: yes;"> </span></span></div>
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<span lang="EN-US" style="mso-ansi-language: EN-US;">We could also want to make three different predictions. Instead of one hypothesis (“something will happen”) we have three different hypotheses, and predict there will be an effect on dv1, dv2, and dv3. The criterion for each <i>t</i>-test is the same, but we now have three hypotheses to evaluate (H1, H2, and H3). Each of these claims can be corroborated, or not. </span></div>
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<span lang="EN-US" style="mso-ansi-language: EN-US;">Scienceverse allows you to specify your hypotheses tests unambiguously (for code used in this blog, see the bottom of the post). It also allows you to simulate a dataset, which we can use to examine Type 1 errors by simulating data where no true effects exist. Finally, scienceverse allows you to run the pre-specified analyses on the (simulated) data, and will automatically create a report that summarizes which hypotheses were corroborated (which is useful when checking if the conclusions in a manuscript indeed follow from the preregistered analyses, or not). The output a single simulated dataset for the scenario where we will interpret any effect on the three dv’s as support for the hypothesis looks like this:</span><br />
<br />
<h1 class="title toc-ignore">
Evaluation of Statistical Hypotheses</h1>
<h4 class="date">
12 March, 2020</h4>
<div class="section level3" id="simulating-null-effects-postregistration">
<h3>
Simulating Null Effects Postregistration</h3>
<div class="section level4" id="author">
<div class="section level2" id="results">
<h2>
Results</h2>
<div class="section level3" id="hypothesis-1-h1">
<h3>
Hypothesis 1: H1</h3>
Something will happen<br />
<ul>
<li><code>p_t_1</code> is confirmed if analysis <a href="https://www.blogger.com/blogger.g?blogID=987850932434001559#ttest_1">ttest_1</a> yields <code>p.value<0.05</code><br />
<br />
The result was p.value = 0.452 (<span style="color: red;">FALSE</span>)<br />
<br />
</li>
<li><code>p_t_2</code> is confirmed if analysis <a href="https://www.blogger.com/blogger.g?blogID=987850932434001559#ttest_2">ttest_2</a> yields <code>p.value<0.05</code><br />
<br />
The result was p.value = 0.21 (<span style="color: red;">FALSE</span>)<br />
<br />
</li>
<li><code>p_t_3</code> is confirmed if analysis <a href="https://www.blogger.com/blogger.g?blogID=987850932434001559#ttest_3">ttest_3</a> yields <code>p.value<0.05</code><br />
<br />
The result was p.value = 0.02 (<span style="color: green;">TRUE</span>)</li>
</ul>
<div class="section level4" id="corroboration-true">
<h4>
Corroboration (<span style="color: green;"> TRUE </span>)</h4>
The hypothesis is corroborated if anything is significant.<br />
<pre><code> p_t_1 | p_t_2 | p_t_3 </code></pre>
</div>
<div class="section level4" id="falsification-false">
<h4>
Falsification (<span style="color: red;"> FALSE </span>)</h4>
The hypothesis is falsified if nothing is significant.<br />
<pre><code> !p_t_1 & !p_t_2 & !p_t_3 </code></pre>
<span style="color: green;"><b>All criteria were met for corroboration.</b></span></div>
</div>
</div>
</div>
</div>
</div>
<br />
<br />
<br />
We see the hypothesis that ‘something will happen’ is corroborated, because there was a significant difference on dv3 - even though this was a Type 1 error, since we simulated data with a true effect size of 0 - and any difference was taken as support for the prediction. With a 5% alpha level, we will observe 1-(1-0.05)^3 = 14.26% Type 1 errors in the long run. This Type 1 error inflation can be prevented by lowering the alpha level, for example by a Bonferroni correction (0.05/3), after which the expected Type 1 error rate is 4.92% (see Bretz et al., 2011, for more advanced techniques to control error rates). When we examine the report for the second scenario, where each dv tests a unique hypothesis, we get the following output from scienceverse:<br />
<br />
<br />
<h1 class="title toc-ignore">
Evaluation of Statistical Hypotheses</h1>
<h4 class="date">
12 March, 2020</h4>
<div class="section level3" id="simulating-null-effects-postregistration">
<h3>
Simulating Null Effects Postregistration</h3>
<div class="section level4" id="author">
<div class="section level2" id="results">
<h2>
Results</h2>
<div class="section level3" id="hypothesis-1-h1">
<h3>
Hypothesis 1: H1</h3>
dv1 will show an effect<br />
<ul>
<li><code>p_t_1</code> is confirmed if analysis <a href="https://www.blogger.com/blogger.g?blogID=987850932434001559#ttest_1">ttest_1</a> yields <code>p.value<0.05</code><br />
<br />
The result was p.value = 0.452 (<span style="color: red;">FALSE</span>)</li>
</ul>
<div class="section level4" id="corroboration-false">
<h4>
Corroboration (<span style="color: red;"> FALSE </span>)</h4>
The hypothesis is corroborated if dv1 is significant.<br />
<pre><code> p_t_1 </code></pre>
</div>
<div class="section level4" id="falsification-true">
<h4>
Falsification (<span style="color: green;"> TRUE </span>)</h4>
The hypothesis is falsified if dv1 is not significant.<br />
<pre><code> !p_t_1 </code></pre>
<span style="color: red;"><b>All criteria were met for falsification.</b></span></div>
</div>
<div class="section level3" id="hypothesis-2-h2">
<h3>
Hypothesis 2: H2</h3>
dv2 will show an effect<br />
<ul>
<li><code>p_t_2</code> is confirmed if analysis <a href="https://www.blogger.com/blogger.g?blogID=987850932434001559#ttest_2">ttest_2</a> yields <code>p.value<0.05</code><br />
<br />
The result was p.value = 0.21 (<span style="color: red;">FALSE</span>)</li>
</ul>
<div class="section level4" id="corroboration-false-1">
<h4>
Corroboration (<span style="color: red;"> FALSE </span>)</h4>
The hypothesis is corroborated if dv2 is significant.<br />
<pre><code> p_t_2 </code></pre>
</div>
<div class="section level4" id="falsification-true-1">
<h4>
Falsification (<span style="color: green;"> TRUE </span>)</h4>
The hypothesis is falsified if dv2 is not significant.<br />
<pre><code> !p_t_2 </code></pre>
<span style="color: red;"><b>All criteria were met for falsification.</b></span></div>
</div>
<div class="section level3" id="hypothesis-3-h3">
<h3>
Hypothesis 3: H3</h3>
dv3 will show an effect<br />
<ul>
<li><code>p_t_3</code> is confirmed if analysis <a href="https://www.blogger.com/blogger.g?blogID=987850932434001559#ttest_3">ttest_3</a> yields <code>p.value<0.05</code><br />
<br />
The result was p.value = 0.02 (<span style="color: green;">TRUE</span>)</li>
</ul>
<div class="section level4" id="corroboration-true">
<h4>
Corroboration (<span style="color: green;"> TRUE </span>)</h4>
The hypothesis is corroborated if dv3 is significant.<br />
<pre><code> p_t_3 </code></pre>
</div>
<div class="section level4" id="falsification-false">
<h4>
Falsification (<span style="color: red;"> FALSE </span>)</h4>
The hypothesis is falsified if dv3 is not significant.<br />
<pre><code> !p_t_3 </code></pre>
<span style="color: green;"><b>All criteria were met for corroboration.</b></span></div>
</div>
</div>
</div>
</div>
<br />
<br />
We now see that two hypotheses were falsified (yes, yes, I know you should not use <i>p</i> > 0.05 to falsify a prediction in real life, and this part of the example is formally wrong so I don't also have to explain equivalence testing to readers not familiar with it - if that is you, <a href="https://journals.sagepub.com/doi/full/10.1177/2515245918770963">read this</a>, and know scienceverse will allow you to specify equivalence test as the criterion to falsify a prediction, see the example <a href="https://scienceverse.github.io/scienceverse/articles/files/prereg.html">here</a>). The third hypothesis is corroborated, even though, as above, this is a Type 1 error. <br />
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It might seem that the second approach, specifying each dv as it’s own hypothesis, is the way to go if you do not want to lower the alpha level to control for multiple comparisons. But take a look at the report of the study you have performed. You have made 3 predictions, of which 1 was corroborated. That is not an impressive success rate. Sure, <a href="https://journals.sagepub.com/doi/full/10.1177/1948550617693058">mixed results happen</a>, and you should interpret results not just based on the <i>p</i>-value (but on the strength of the experimental design, assumptions about power, your prior, the strength of the theory, etc.) but if these predictions were derived from the same theory, this set of results is not particularly impressive. Since researchers can never selectively report only those results that ‘work’ because this would be a violation of the code of research integrity, we should always be able to see the meager track record of predictions.If you don't feel ready to make a specific predictions (and run the risk of sullying your track record) either do unplanned exploratory tests, and do not make claims based on their results, or preregister all possible tests you can think of, and massively lower your alpha level to control error rates (for example, genome-wide association studies sometimes use an <a href="http://www.ph.ucla.edu/epi/faculty/zhang/Webpages/zhang/courses/epi243_07/readings/gwas_review2.pdf">alpha level</a> of 5 x 10–8 to control the Type 1 erorr rate). <br />
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<div class="MsoBibliography">
<span lang="EN-US" style="mso-ansi-language: EN-US;">Hopefully, specifying our hypotheses (and what would corroborate them) transparently by using scienceverse makes it clear what happens in the long run in both scenarios. In the long run, both the first scenario, if we would use an alpha level of 0.05/3 instead of 0.05, and the second scenario, with an alpha level of 0.05 for each individual hypothesis, will lead to the same end result: Not more than 5% of our claims will be wrong, if the null hypothesis is true. In the first scenario, we are making one claim in an experiment, and in the second we make three. In the second scenario we will end up with more false claims in an absolute sense, but the relative number of false claims is the same in both scenarios. And that’s exactly the goal of family-wise error control. </span></div>
<div class="MsoBibliography">
<br /></div>
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<span lang="EN-US" style="line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: Arial; mso-bidi-font-size: 12.0pt;"><b>References</b> </span></div>
<div class="MsoBibliography">
<span lang="EN-US" style="line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: Arial; mso-bidi-font-size: 12.0pt;">Bender, R., & Lange, S. (2001). Adjusting for multiple testing—When and how? <i>Journal of Clinical Epidemiology</i>, <i>54</i>(4), 343–349.</span></div>
<div class="MsoBibliography">
<span lang="EN-US" style="line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: Arial; mso-bidi-font-size: 12.0pt;">Bretz, F., Hothorn, T., & Westfall, P. H. (2011). <i>Multiple comparisons using R</i>. CRC Press.</span></div>
<div class="MsoBibliography">
<span lang="EN-US" style="line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: Arial; mso-bidi-font-size: 12.0pt;">Mayo, D. G. (2018). <i>Statistical inference as severe testing: How to get beyond the statistics wars</i>. Cambridge University Press.</span></div>
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<span lang="EN-US" style="line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: Arial; mso-bidi-font-size: 12.0pt;">Neyman, J. (1957). “Inductive Behavior” as a Basic Concept of Philosophy of Science. <i>Revue de l’Institut International de Statistique / Review of the International Statistical Institute</i>, <i>25</i>(1/3), 7. https://doi.org/10.2307/1401671</span><br />
<br />
<span lang="EN-US" style="line-height: 107%; mso-ansi-language: EN-US; mso-bidi-font-family: Arial; mso-bidi-font-size: 12.0pt;"><i>Thanks to Lisa DeBruine for feedback on an earlier draft of this blog post.</i> </span></div>
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<script src="https://gist.github.com/Lakens/06c2d69da5fa5200c19ef825bbff7f01.js"></script><br />
<script src="https://gist.github.com/Lakens/32ba5489791bac47c8f33cf89304f73d.js"></script>Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com1tag:blogger.com,1999:blog-987850932434001559.post-22338159451028424252020-02-02T07:09:00.001+01:002020-02-02T07:16:20.026+01:00Review of "Do Effect Sizes in Psychology Laboratory Experiments Mean Anything in Reality?"<i>Researchers spend a lot of time reviewing papers. These reviews are rarely made public. Sometimes reviews might be useful for readers of an article. Here, I'm sharing my review of "</i><span class="css-901oao css-16my406 r-1qd0xha r-ad9z0x r-bcqeeo r-qvutc0"><a href="https://psyarxiv.com/mpw4t/" target="_blank">Do Effect Sizes in Psychology Laboratory Experiments Mean Anything in Reality?</a>"</span> <i>by Roy Baumeister. I reviewed this (blinded) manuscript in December 2019 for a journal where it was rejected January 8 based on 2 reviews. Below you can read the review as I submitted it. I am sharing this review because the paper was accepted at another journal. </i> <br />
<br />
In this opinion piece the authors try to argue for the lack of theoretical meaning of effect sizes in psychology. The opinion piece makes a point I think most reasonable people already agreed upon (social psychology makes largely ordinal predictions). The question is how educational and well argued their position is that this means effect sizes are theoretically not that important. On that aspect, I found the paper relatively weak. Too many statements are overly simplistic and the text is far behind on the state of the art (it reads as if it was written 20 years ago). I think a slightly brushed up version might make a relatively trivial but generally educational point for anyone not up to speed on this topic. If the author put in a bit more effort to have a discussion that incorporates the state of the art, this could be a more nuanced piece that has a bit stronger analysis of the issues at play, and a bit more vision about where to go. I think the latter would be worth reading for a general audience at this journal. <br />
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<span lang="EN-US">Main points. </span></div>
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<h4>
<span lang="EN-US">1) What is the real reason our theories do
not predict effect sizes? </span></h4>
</div>
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<span lang="EN-US">The authors argue how most theories in
social psychology are verbal theories. I would say most verbal theories in
social psych are actually not theories </span><span lang="EN-US" style="mso-bidi-font-family: "Times New Roman";">(Fiedler, 2004)</span><span lang="EN-US"> but closer to tautologies. That being said, I think the anecdotal
examples the authors use throughout their paper (obedience effects, bystander
effect, cognitive dissonance) are all especially weak, and the paper would be
improved if all these examples are removed. Although the authors are correct in
stating we hardly care about the specific effect size in those studies, I am
not interested in anecdotes of studies where we know we do not care about
effect sizes. This is a weak (as in, not severe) test of the argument the
authors are making, with confirmation bias dripping from every sentence. If you
want to make a point about effect sizes not mattering, you can easily find situations
where they do not theoretically matter. But this is trivial and boring. What
would be more interesting is an analysis of why we do not care about effect
sizes that generalizes beyond the anecdotal examples. The authors are close,
but do not provide such a general argument yet. One reason I think the authors
would like to mention is that there is a measurement crisis in psych – people
use a hodgepodge of often completely unvalidated measures. It becomes a lot
more interesting to quantify effect sizes if we all use the same measurement.
This would also remove the concern about standardized vs unstandardized effect
sizes. But more generally, I think the authors should make an argument more
from basic principles, than based on anecdotes, if they want to be convincing. </span></div>
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<span lang="EN-US">2) When is something an ordinal prediction?</span></h4>
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<span lang="EN-US">Now, if we use the same measures, are there
cases where we predict effect sizes? The authors argue we never predict an
exact effect size. True, but again, uninteresting. We can predict a range of
effect sizes. The authors cite Meehl, but they should really incorporate
Meehl’s point from his 1990 paper. Predicting a range of effect sizes is
already quite something, and the authors do not give this a fair discussion. It
matters a lot if I predict an effect in the range of 0.2 to 0.8, even though
this is very wide, then if I say I predict any effect larger than zero. Again,
a description of the state of the art is missing in the paper. This question
has been discussed in many literatures the authors do not mention. The issue is
the same as the discussion about whether we should use a uniform prior in
Bayesian stats, or a slightly more informative prior, because we predict
effects in some range. My own work specifying the smallest effect size of
interest also provides quite a challenge to the arguments of the current
authors. See especially the example of Burriss and colleages in our 2018 paper </span><span lang="EN-US" style="mso-bidi-font-family: "Times New Roman";">(Lakens, Scheel,
& Isager, 2018)</span><span lang="EN-US">. They
predicted an effect should be noticeable with the naked eye, and it is an
example where a theory very clearly makes a range prediction, falsifying the
authors arguments in the current paper. That these cases exist, means the
authors are completely wrong in their central thesis. It also means they need
to rephrase their main argument – when do we have range predictions, and when
are we predicting *any* effect that is not zero. And why? I think many theories
in psychology would argue that effects should be larger than some other effect
size. This can be sufficient to make a valid range prediction. Similarly, if
psychologist would just think about effect sizes that are too large, we would
not have papers in PNAS edited by Nobel prize winners that think </span>the effect of judges on parole decisions over time is a psychological mechanism, when the effect size is too large to be plausible (Glöckner, 2016). So effects should often be larger or smaller than some value, and this does not align with the current argument by the authors.<span lang="EN-US"><span style="mso-spacerun: yes;"> </span></span><br />
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<span lang="EN-US">I would argue the fact that psych theories
predict range effects means psych theories make effect size predictions that
are relevant enough to quantify. We do a similar thing when we compare 2
conditions, for example when we predict an effect is larger in condition X than
Y. In essence, this means we say: We predict the effect size of X to be in a
range that is larger than the effect size of Y. Now, this is again a range
prediction. We do not just say both X and Y have effects that differ from zero.
It is still an ordinal prediction, so fits with the basic point of the authors
about how we predict, but it no longer fits with their argument that we simply
test for significance. Ordinal predictions can be more complex than the authors
currently describe. To make a solid contribution they will need to address what
ordinal predictions are in practice. With the space that is available after the
anecdotes are removed, they can add a real analysis of how we test hypotheses
in general, where range predictions fit with ordinal predictions, and if we
would use the same measures and have some widely used paradigms, we could, if
we wanted to, create theories that make quantifiable range predictions. I agree
with the authors it can be perfectly valid to choose a unique
operationalization of a test, and that this allows you to increase or decrease
the effect size depending on the operationalization. This is true. But we can
make theories that predict things beyond just any effect if we fix our
paradigms and measures – and authors should discuss if this might be desirable
to give their own argument a bit more oomph and credibility. If the authors
want to argue that standard measures in psychology are undesirable or
impossible, that might be interesting, but I doubt it will work. And thus, I
expect author will need to give more credit to the power of ordinal
predictions. In essence, my point here is that if you think we can not make a
range prediction on a standardized measure, you also think there can be no
prediction that condition X yields a larger effect than condition y, and yet we
make these predictions all the time. Again, in a Stroop effect with 10 trials
effect sizes differ than is we have 10000 trials – but given a standardized
measure, we can predict relative differences. </span></div>
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<span lang="EN-US">3) Standardized vs unstandardized effect
sizes</span></h4>
</div>
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<span lang="EN-US">I might be a bit too rigid here, but when
scientists make claims, I like them to be accompanied by evidence. The authors
write “Finally, it is commonly believed that in the case of arbitrary units, a
standardized effect size is more meaningful and informative than its equivalent
in raw score units.” There is no citation and this to me sounds 100% incorrect.
I am sure they might be able to dig out one misguided paper making this claim.
But this is not commonly believed, and the literature abundantly shows researchers
argue the opposite – the most salient example is </span><span lang="EN-US" style="mso-bidi-font-family: "Times New Roman";">(Baguley, 2009)</span><span lang="EN-US"> but any stats book would suffice. The lack of a citation to Baguley
is just one of the examples where the authors seem not to be up to speed of the
state of the art, and where their message is not nuanced enough, while the
discussion in the literature surpassed many of their simple claims more than a
decade ago. I think the authors should improve their discussion of standardized
and unstandardized effect sizes. Standardized effect sizes are useful of
measurement tools differ, and if you have little understanding of what you are
measuring. Although I think this is true in general in social psychology (and
the measurement crisis is real), I think the authors are not making the point
that *given* that social psychology is such a mess when it comes to how
researchers in the field measure things, we can not make theoretically
quantifiable predictions. I would agree with this. I think they try to argue
that even if social psychology was not such a mess, we could still not make quantifiable
predictions. I disagree. Issues related to the standardized and unstandardized
effect sizes are a red herring. They do not matter anything. If we understood
our measures and standardized them, we would have accurate estimates of the
sd’s for what we are interested in, and this whole section can just be deleted.
The authors should be clear if they think we will never standardize our
measures and there is no value in them or if it is just difficult in practice
right now. Regardless, they issue with standardized effects is mute, since
their first sentence that standardized effect sizes are more meaningful is just
wrong </span><span lang="EN-US" style="mso-bidi-font-family: "Times New Roman";">(for a discussion, Lakens,
2013)</span><span lang="EN-US">. </span></div>
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<h4>
<span lang="EN-US">Minor points</span></h4>
</div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-US">When discussing Festinger and Carlsmith, it
makes sense to point out how low quality and riddled with mistakes the study
was: <a href="https://mattiheino.com/2016/11/13/legacy-of-psychology/">https://mattiheino.com/2016/11/13/legacy-of-psychology/</a>.
</span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-US">The authors use the first studies of several
classic research lines as an example that psychology predicts directional
effects at best, and that these studies cared about demonstrating an effect.
Are the authors sure their idea of scientific progress is that we for ever
limit ourselves to demonstrating effects? This is criticized in many research
fields, and the idea of developing computational models in in some domains
deserves to be mentioned. Even a simple stupid model can make range predictions
that can be tested theoretically. A broader discussion of psychologists who
have a bit more ambition for social psychology than the current authors, and
who believe that some progress towards even a rough computational model would
allow us to predict not just ranges, but also the shapes of effects (e.g., linear
vs exponential effects) would be warranted, I think. I think it is fine if the
authors have the opinion that social psychology will not move along by more quantification.
But I find the final paragraph a bit vague and uninspiring in what the vision
is. No one argues against practical applications or conceptual replications. The
authors rightly note it is easier (although I think not as much easier as the
authors think) to use effects in cost-benefit analyses in applied research. But
what is the vision? Demonstration proofs and an existentialistic leap of faith
that we can apply things? That has not worked well. Applied psychological researchers
have rightly criticized theoretically focused social psychologists for
providing basically completely useless existence proofs that often do not
translate to any application, and are too limited to be of any value. I do not
know what the solution is here, but I would be curious to hear if the authors
have a slightly more ambitious vision. If not, that is fine, but if they have
one, I think it would boost the impact of the paper. </span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoNormal">
<span lang="EN-US">Signed,</span></div>
<div class="MsoNormal">
<span lang="EN-US">Daniel Lakens</span></div>
<div class="MsoNormal">
<br /></div>
<div class="MsoBibliography">
<span lang="EN-US" style="mso-bidi-font-family: "Times New Roman";">Baguley, T. (2009).
Standardized or simple effect size: What should be reported? <i>British Journal
of Psychology</i>, <i>100</i>(3), 603–617.
https://doi.org/10.1348/000712608X377117</span></div>
<div class="MsoBibliography">
<span lang="EN-US" style="mso-bidi-font-family: "Times New Roman";">Fiedler,
K. (2004). Tools, toys, truisms, and theories: Some thoughts on the creative
cycle of theory formation. <i>Personality and Social Psychology Review</i>, <i>8</i>(2),
123–131. https://doi.org/10.1207/s15327957pspr0802_5</span></div>
<div class="MsoBibliography">
<span lang="EN-US" style="mso-bidi-font-family: "Times New Roman";">Glöckner,
A. (2016). The irrational hungry judge effect revisited: Simulations reveal
that the magnitude of the effect is overestimated. <i>Judgment and Decision
Making</i>, <i>11</i>(6), 601–610.</span></div>
<div class="MsoBibliography">
<span lang="EN-US" style="mso-bidi-font-family: "Times New Roman";">Lakens,
D. (2013). Calculating and reporting effect sizes to facilitate cumulative
science: A practical primer for t-tests and ANOVAs. <i>Frontiers in Psychology</i>,
<i>4</i>. https://doi.org/10.3389/fpsyg.2013.00863</span></div>
<div class="MsoBibliography">
<span lang="EN-US" style="mso-bidi-font-family: "Times New Roman";">Lakens,
D., Scheel, A. M., & Isager, P. M. (2018). Equivalence Testing for
Psychological Research: A Tutorial. <i>Advances in Methods and Practices in
Psychological Science</i>, <i>1</i>(2), 259–269.
https://doi.org/10.1177/2515245918770963</span></div>
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<br /></div>
Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com0tag:blogger.com,1999:blog-987850932434001559.post-20282454046149074962020-01-20T11:53:00.002+01:002020-06-07T21:48:18.109+02:00Review of "The Generalizability Crisis" by Tal Yarkoni<div><i>A response to this blog by Tal Yarkoni is <a href="https://www.talyarkoni.org/blog/2020/05/06/induction-is-not-optional-if-youre-using-inferential-statistics-reply-to-lakens/" target="_blank">here</a>.</i><br /></div><div><br /></div><div>In a recent preprint titled "<a href="https://psyarxiv.com/jqw35">The Generalizability Crisis</a>", Tal Yarkoni examines whether the current practice of how psychologists generalize from studies to theories is problematic. He writes: “The question taken up in this paper is whether or not the tendency to generalize psychology findings far beyond the circumstances in which they were originally established is defensible. The case I lay out in the next few sections is that it is not, and that unsupported generalization lies at the root of many of the methodological and sociological challenges currently affecting psychological science.” We had a <a href="https://twitter.com/lakens/status/1200797620953858050?s=20" target="_blank">long twitter discussion</a> about the paper, and then read it in our reading group. In this review, I try to make my thoughts about the paper clear in one place, which might be useful if we want to continue to discuss whether there is a generalizability crisis, or not.</div>
<br />
First, I agree with Yarkoni that almost all the proposals he makes in the section “Where to go from here?” are good suggestions. I don’t think they follow logically from his points about generalizability, as I detail below, but they are nevertheless solid suggestions a researcher should consider. Second, I agree that there are research lines in psychology where modelling more things as random factors will be productive, and a forceful manifesto (even if it is slightly less practical than similar earlier papers) might be a wake up call for people who had ignored this issue until now. <br />
<br />
Beyond these two points of agreement, I found the main thesis in his article largely unconvincing. I don’t think there is a generalizability crisis, but the article is a nice illustration of why philosophers like Popper abandoned the idea of an inductive science. When Yarkoni concludes that “A direct implication of the arguments laid out above is that a huge proportion of the quantitative inferences drawn in the published psychology literature are so inductively weak as to be at best questionable and at worst utterly insensible.” I am primarily surprised he believes induction is a defensible philosophy of science. There is a very brief discussion of views by Popper, Meehl, and Mayo on page 19, but their work on testing theories is proposed as a probable not feasible solution – which is peculiar, because these authors would probably disagree with most of the points made by Yarkoni, and I would expect at least somewhere in the paper a discussion comparing induction against the deductive approach (especially since the deductive approach is arguably the dominant approach in psychology, and therefore none of the generalizability issues raised by Yarkoni are a big concern). Because I believe the article starts from a faulty position (scientists are not concerned with induction, but use deductive approaches) and because Yarkoni provides no empirical support for any of his claims that generalizability has led to huge problems (such as incredibly high Type 1 error rates), I remain unconvinced there is anything remotely close to the generalizability crisis he so evocatively argues for. The topic addressed by Yarkoni is very broad. It probably needs a book length treatment to do it justice. My review is already way too long, and I did not get into the finer details of the argument. But I hope this review helps to point out the parts of the manuscript where I feel important arguments lack a solid foundation, and where issues that deserve to be discussed are ignored.<br />
<div>
<br />
<h4>
<b>Point 1: “Fast” and “slow” approaches need some grounding in philosophy of science. </b></h4>
<br />
Early in the introduction, Yarkoni says there is a “fast” and “slow” approach of drawing general conclusions from specific observations. Whenever people use words that don’t exactly describe what they mean, putting them in quotation marks is generally not a good idea. The “fast” and “slow” approaches he describes are not, I believe upon closer examination, two approaches “of drawing general conclusions from specific observations”. <br />
<br />
The difference is actually between induction (the “slow” approach of generalizing from single observations to general observations) and deduction, as proposed by for example Popper. As Popper writes “According to the view that will be put forward here, the method of critically testing theories, and selecting them according to the results of tests, always proceeds on the following lines. From a new idea, put up tentatively, and not yet justified in any way—an anticipation, a hypothesis, a theoretical system, or what you will—conclusions are drawn by means of logical deduction.” <br />
<br />
Yarkoni incorrectly suggests that “upon observing that a particular set of subjects rated a particular set of vignettes as more morally objectionable when primed with a particular set of cleanliness-related words than with a particular set of neutral words, one might draw the extremely broad conclusion that ‘cleanliness reduces the severity of moral judgments’”. This reverses the scientific process as proposed by Popper, which is (as several people have argued, see below) the dominant approach to knowledge generation in psychology. The authors are not concluding that “cleanliness reduces the severity of moral judgments” from their data. This would be induction. Instead, they are positing that “cleanliness reduces the severity of moral judgments”, they collected data and performed and empirical test, and found their hypothesis was corroborated. In other words, the hypothesis came first. It is not derived from the data – the hypothesis is what led them to collect the data. <br />
<br />
Yarkoni deviates from what is arguably the common approach in psychological science, and suggests induction might actually work: “Eventually, if the effect is shown to hold when systematically varying a large number of other experimental factors, one may even earn the right to summarize the results of a few hundred studies by stating that “cleanliness reduces the severity of moral judgments””. This approach to science flies right in the face of Popper (1959/2002, p. 10), who says: “I never assume that we can argue from the truth of singular statements to the truth of theories. I never assume that by force of ‘verified’ conclusions, theories can be established as ‘true’, or even as merely ‘probable’.” Similarly, Lakatos (1978, p. 2) writes: “One can today easily demonstrate that there can be no valid derivation of a law of nature from any finite number of facts; but we still keep reading about scientific theories being proved from facts. Why this stubborn resistance to elementary logic?” I am personally on the side of Popper and Lakatos, but regardless of my preferences, Yarkoni needs to provide some argument his inductive approach to science has any possibility of being a success, preferably by embedding his views in some philosophy of science. I would also greatly welcome learning why Popper and Lakatos are wrong. Such an argument, which would overthrow the dominant model of knowledge generation in psychology, could be impactful, although a-priori I doubt it will be very successful.<br />
<div>
<br />
<h4>
Point 2: Titles are not evidence for psychologist’s tendency to generalize too quickly.</h4>
<div>
<br /></div>
This is a minor point, but I think a good illustration of the weakness of some of the main arguments that are made in the paper. On the second page, Yarkoni argues that “the vast majority of psychological scientists have long operated under a regime of (extremely) fast generalization”. I don’t know about the vast majority of scientists, but Yarkoni himself is definitely using fast generalization. He looked through a single journal, and found 3 titles that made general statements (e.g., “Inspiration Encourages Belief in God”). When I downloaded and read this article, I noticed the discussion contains a ‘constraint on generalizability’ in the discussion, following (Simons et al., 2017). The authors wrote: “We identify two possible constraints on generality. First, we tested our ideas only in American and Korean samples. Second, we found that inspiring events that encourage feelings of personal insignificance may undermine these effects.”. Is Yarkoni not happy with these two sentence clearly limiting the generalizability in the discussion? <br />
<br />
For me, this observation raised serious concerns about the statement Yarkoni makes that, simply from the titles of scientific articles, we can make a statement about whether authors make ‘fast’ or ‘slow’ generalizations. One reason is that Yarkoni examined titles from a scientific article that adheres to the publication manual of the APA. In the section on titles, the APA states: “A title should summarize the main idea of the manuscript simply and, if possible, with style. It should be a concise statement of the main topic and should identify the variables or theoretical issues under investigation and the relationship between them. An example of a good title is "Effect of Transformed Letters on Reading Speed."”. To me, it seems the authors are simply following the APA publication manual. I do not think their choice for a title provides us with any insight whatsoever about the tendency of authors to have a preference for ‘fast’ generalization. Again, this might be a minor point, but I found this an illustrative example of the strength of arguments in other places (see the next point for the most important example). Yarkoni needs to make a case that scientists are overgeneralizing, for there to be a generalizability crisis – but he does so unconvincingly. I sincerely doubt researchers expect their findings to generalize to all possible situations mentioned in the title, I doubt scientists believe titles are the place to accurately summarize limits of generalizability, and I doubt Yarkoni has made a strong point that psychologists overgeneralize based on this section. More empirical work would be needed to build a convincing case (e.g., code how researchers actually generalize their findings in a random selection of 250 articles, taking into account Gricean communication norms (especially the cooperative principle) in scientific articles).<br />
<div>
<br />
<h4>
Point 3: Theories and tests are not perfectly aligned in deductive approaches.</h4>
<div>
<br /></div>
After explaining that psychologists use statistics to test predictions based on experiments that are operationalizations of verbal theories, Yarkoni notes: “From a generalizability standpoint, then, the key question is how closely the verbal and quantitative expressions of one’s hypothesis align with each other.”<br />
<br />
Yarkoni writes: “<i>When a researcher verbally expresses a particular hypothesis, she is implicitly defining a set of admissible observations containing all of the hypothetical situations in which some measurement could be taken that would inform that hypothesis. <b>If the researcher subsequently asserts that a particular statistical procedure provides a suitable test of the verbal hypothesis, she is making the tacit but critical assumption that the universe of admissible observations implicitly defined by the chosen statistical procedure (in concert with the experimental design, measurement model, etc.) is well aligned with the one implicitly defined by the qualitative hypothesis. </b>Should a discrepancy between the two be discovered, the researcher will then face a choice between (a) working to resolve the discrepancy in some way (i.e., by modifying either the verbal statement of the hypothesis or the quantitative procedure(s) meant to provide an operational parallel); or (b) giving up on the link between the two and accepting that the statistical procedure does not inform the verbal hypothesis in a meaningful way.</i>” <br />
<br />
I highlighted what I think is the critical point is in a bold font. To generalize from a single observation to a general theory through induction, the sample and the test should represent the general theory. This is why Yarkoni is arguing that there has to be a direct correspondence between the theoretical model, and the statistical test. This is true in induction. <br />
<br />
If I want to generalize beyond my direct observations, which are rarely sampled randomly from all possible factors that might impact my estimate, I need to account for uncertainty in the things I have not observed. As Yarkoni clearly explains, one does this by adding random factors to a model. He writes (p. 7) “<i>Each additional random factor one adds to a model licenses generalization over a corresponding population of potential measurements, expanding the scope of inference beyond only those measurements that were actually obtained. However, adding random factors to one’s model also typically increases the uncertainty with which the fixed effects of interest are estimated</i>”. You don’t need to read Popper to see the problem here – if you want to generalize to all possible random factors, there are so many of them, you will never be able to overcome the uncertainty and learn anything. This is why inductive approaches to science have largely been abandoned. As Yarkoni accurately summarizes based on an large multi-lab study on verbal overshadowing by Alogna: “given very conservative background assumptions, the massive Alogna et al. study—an initiative that drew on the efforts of dozens of researchers around the world—does not tell us much about the general phenomenon of verbal overshadowing. Under more realistic assumptions, it tells us essentially nothing.” This is also why Yarkoni’s first practical recommendation on how to move forward is to not solve the problem, but to do something else: “One perfectly reasonable course of action when faced with the difficulty of extracting meaningful, widely generalizable conclusions from effects that are inherently complex and highly variable is to opt out of the enterprise entirely.” <br />
<br />
This is exactly the reason Popper (among others) rejected induction, and proposed a deductive approach. Why isn’t the alignment between theories and tests raised by Yarkoni a problem for the deductive approach proposed by Popper, Meehl, and Mayo? The reason is that the theory is tentatively posited as true, but in no way believed to be a complete representation of reality. This is an important difference. Yarkoni relies on an inductive approach, and thus the test needs to be aligned with the theory, and the theory defines “a set of admissible observations containing all of the hypothetical situations in which some measurement could be taken that would inform that hypothesis.” For deductive approaches, this is not true. <br />
<br />
For philosophers of science like Popper and Lakatos, a theory is not a complete description of reality. Lakatos writes about theories: “Each of them, at any stage of its development, has unsolved problems and undigested anomalies. All theories, in this sense, are born refuted and die refuted.” Lakatos gives the example that Newton’s Principia could not even explain the motion of the moon when it was published. The main point here: All theories are wrong. The fact that all theories (or models) are wrong should not be surprising. Box’s quote “All models are wrong, some are useful” is perhaps best known, but I prefer Box (1976) on parsimony: “Since all models are wrong the scientist cannot obtain a "correct" one by excessive elaboration. On the contrary following William Ockham (1285-1349) he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so overelaboration and overparameterization is often the mark of mediocrity (Ockham's knife).” He follows this up by stating “Since all models are wrong the scientist must be alert to what is importantly wrong. It is inappropriate to be concerned about mice when there are tigers abroad.” <br />
<br />
In a deductive approach, the goal of a theoretical model is to make useful predictions. I doubt anyone believes that any of the models they are currently working on is complete. Some researchers might follow an instrumentalist philosophy of science, and don’t expect their theories to be anything more than useful tools. Lakatos’s (1978) main contribution to philosophy of science was to develop a way we deal with our incorrect theories, admitting that all needed adjustment, but some adjustments lead to progressive research lines, and others to degenerative research lines. <br />
<br />
In a deductive model, it is perfectly fine to posit a theory that eating ice-cream makes people happy, without assuming this holds for all flavors, across all cultures, at all temperatures, and is irrespective of the amount of ice-cream eaten previously, and many other factors. After all, it is just a tentatively model that we hope is simple enough to be useful, and that we expect to become more complex as we move forward. As we increase our understanding of food preferences, we might be able to modify our theory, so that it is still simple, but also allows us to predict the fact that eggnog and bacon flavoured ice-cream do not increase happiness (on average). The most important thing is that our theory is tentative, and posited to allow us to make good predictions. As long as the theory is useful, and we have no alternatives to replace it with, the theory will continue to be used – without any expectation that is will generalize to all possible situations. As Box (1976) writes: “Matters of fact can lead to a tentative theory. Deductions from this tentative theory may be found to be discrepant with certain known or specially acquired facts. These discrepancies can then induce a modified, or in some cases a different, theory.” A discussion of this large gap between Yarkoni and deductive approaches proposed by Popper and Meehl, where Yarkoni thinks theories and tests need to align, and deductive approaches see theories as tentative and wrong, should be included, I think. </div>
<div>
<br />
<br />
<h4>
Point 4: The dismissal of risky predictions is far from convincing (and generalizability is typically a means to risky predictions, not a goal in itself). </h4>
<br />
If we read Popper (but also on the statistical side the work of Neyman) we see induction as a possible goal in science is clearly rejected. Yarkoni mentions deductive approaches briefly in his section on adopting better standards, in the sub-section on making riskier predictions. I intuitively expected this section to be crucial – after all, it finally turns to those scholars who would vehemently disagree with most of Yarkoni’s arguments in the preceding sections – but I found this part rather disappointing. Strangely enough, Yarkoni simply proposes predictions as a possible solution – but since the deductive approach goes directly against the inductive approach proposed by Yarkoni, it seems very weird to just mention risky predictions as one possible solution, when it is actually a completely opposite approach that rejects most of what Yarkoni argues for. Yarkoni does not seem to believe that the deductive mode proposed by Popper, Meehl, and Mayo, a hypothesis testing approach that is arguably the dominant approach in most of psychology (Cortina & Dunlap, 1997; Dienes, 2008; Hacking, 1965), has a lot of potential. The reason he doubts severe tests of predictions will be useful is that “in most domains of psychology, there are pervasive and typically very plausible competing explanations for almost every finding” (Yarkoni, p. 19). This could be resolved if risky predictions were possible, which Yarkoni doubts. <br />
<br />
Yarkoni’s criticism on the possibility of severe tests is regrettably weak. Yarkoni says that “Unfortunately, in most domains of psychology, there are pervasive and typically very plausible competing explanations for almost every finding.” From his references (Cohen, Lykken, Meehl) we can see he refers to the crud factor, or the idea that the null hypothesis is always false. As we recently pointed out in a review paper on crud (Orben & Lakens, 2019), Meehl and Lykken disagreed about the definition of the crud factor, the evidence of crud in some datasets can not be generalized to all studies in pychology, and “The lack of conceptual debate and empirical research about the crud factor has been noted by critics who disagree with how some scientists treat the crud factor as an “axiom that needs no testing” (Mulaik, Raju, & Harshman, 1997).”. Altogether, I am very unconvinced by this cursory reference to crud makes a convincing point that “there are pervasive and typically very plausible competing explanations for almost every finding”. Risky predictions seem possible, to me, and demonstrating the generalizability of findings is actually one way to perform a severe test. <br />
<br />
When Yarkoni discusses risky predictions, he sticks to risky quantitative predictions. As explained in Lakens (2020), “Making very narrow range predictions is a way to make it <i>statistically </i>likely to falsify your prediction if it is wrong. But the severity of a test is determined by all characteristics of a study that increases the capability of a prediction to be wrong, if it is wrong. For example, by predicting you will only observe a statistically significant difference from zero in a hypothesis test if a very specific set of experimental conditions is met that all follow from a single theory, it is possible to make <i>theoretically </i>risky predictions.” I think the reason most psychologists perform studies that demonstrate the generalizability of their findings has nothing to do with their desire to inductively build a theory from all these single observations. They show the findings generalize, because it increases the severity of their tests. In other words, according to this deductive approach, generalizability is not a goal in itself, but a it follows from the goal to perform severe tests. It is unclear to me why Yarkoni does not think that approaches such as triangulation (Munafò & Smith, 2018) are severe tests. I think these approaches are the driving force between many of the more successful theories in social psychology (e.g., social identity theory), and it works fine. <br />
<br />
Generalization as a means to severely test a prediction is common, and one of the goals of direct replications (generalizing to new samples) and conceptual replications (generalizing to different procedures). Yarkoni might disagree with me that generalization serves severity, not vice versa. But then what is missing from the paper is a solid argument why people would want to generalize to begin with, assuming at least a decent number of them do not believe in induction. The inherent conflict between the deductive approaches and induction is also not explained in a satisfactory manner. <br />
<br />
<h4>
Point 5: Why care about statistical inferences, if these do not relate to sweeping verbal conclusions? </h4>
<br />
If we ignore all points previous points, we can still read Yarkoni’s paper as a call to introduce more random factors in our experiments. This nicely complements recent calls to vary all factors you do not thing should change the conclusions you draw (Baribault et al., 2018), and classic papers on random effects (Barr et al., 2013; Clark, 1969; Cornfield & Tukey, 1956). <br />
<br />
Yarkoni generalizes from the fact that most scientists model subjects as a random factor, and then asks why scientists generalize to all sorts of other factors that were not in their models. He asks “Why not simply model all experimental factors, including subjects, as fixed effects”. It might be worth noting in the paper that sometimes researchers model subjects as fixed effects. For example, Fujisaki and Nishida (2009) write: “Participants were the two authors and five paid volunteers” and nowhere in their analyses do they assume there is any meaningful or important variation across individuals. In many perception studies, an eye is an eye, and an ear is an ear – whether from the author, or a random participant dragged into the lab from the corridor. <br />
<br />
In other research areas, we do model individuals as a random factor. Yarkoni says we model stimuli as a random factor because: “The reason we model subjects as random effects is not that such a practice is objectively better, but rather, that this specification more closely aligns the meaning of the quantitative inference with the meaning of the qualitative hypothesis we’re interested in evaluating”. I disagree. I think we model certain factor as random effects because we have a high prior these factors influence the effect, and leaving them out of the model would reduce the strength of our prediction. Leaving them out reduces the probability a test will show we are wrong, if we are wrong. It impacts the severity of the test. Whether or not we need to model factors (e.g., temperature, the experimenter, or day of the week) as random factors because not doing so reduces the severity of a test is a subjective judgments. Research fields need to decide for themselves. It is very well possible more random factors are generally needed, but I don’t know how many, and doubt it will ever be as severe are the ‘generalizability crisis’ suggests. If it is as severe as Yarkoni suggests, some empirical demonstrations of this would be nice. Clark (1973) showed his language-as-fixed-effect fallacy using real data. Barr et al (2013) similarly made their point based on real data. I currently do not find the theoretical point very strong, but real data might convince me otherwise. <br />
<br />
The issues about including random factors is discussed in a more complete, and importantly, applicable, manner in Barr et al (2013). Yarkoni remains vague on which random factors should be included and which not, and just recommends ‘more expansive’ models. I have no idea when this is done satisfactory. This is a problem with extreme arguments like the one Yarkoni puts forward. It is fine in theory to argue your test should align with whatever you want to generalize to, but in practice, it is impossible. And in the end, statistics is just a reasonably limited toolset that tries to steer people somewhat in the right direction. The discussion in Barr et al (2013), which includes trade-offs between converging models (which Yarkoni too easily dismisses as solved by modern computational power – it is not solved) and including all possible factors, and interactions between all possible factors, is a bit more pragmatic. Similarly, Cornfield & Tukey (1956) more pragmatically list options ranging from ignoring factors altogether, to randomizing them, or including them as a factor, and note “Each of these attitudes is appropriate in its place. In every experiment there are many variables which could enter, and one of the great skills of the experimenter lies in leaving out only inessential ones.” Just as pragmatically, Clark (1973) writes: “The wide-spread capitulation to the language-as-fixed-effect fallacy, though alarming, has probably not been disastrous. In the older established areas, most experienced investigators have acquired a good feel for what will replicate on a new language sample and what will not. They then design their experiments accordingly.” As always, it is easy to argue for extremes in theory, but this is generally uninteresting for an applied researcher. It would be great if Yarkoni could provide something a bit more pragmatic about what to do in practice than his current recommendation about fitting “more expansive models” – and provides some indication where to stop, or at least suggestions what an empirical research program would look like that tells us where to stop, and why. In some ways, Yarkoni’s point generalizes the argument that most findings in psychology do not generalize to non-WEIRD populations (Henrich et al., 2010), and it has the same weakness. WEIRD is a nice acronym, but it is just a completely random collection of 5 factors that might limit generalizability. The WEIRD acronym functions more as a nice reminder that boundary conditions exist, but it does not allow us to predict when they exist, or when they matter enough to be included in our theories. Currently, there is a gap between the factors that in theory could matter, and the factors that we should in practice incorporate. Maybe it is my pragmatic nature, but without such a discussion, I think the paper offers relatively little progress compared to previous discussions about generalizability (of which there are plenty). <br />
<br />
<h4>
Conclusion </h4>
<br />
A large part of Yarkoni’s argument is based on the fact that theories and tests should be closely aligned, while in a deductive approach based on severe tests of predictions, models are seen as simple, tentative, and wrong, and this is not considered a problem. Yarkoni does not convincingly argue researchers want to generalize extremely broadly (although I agree papers would benefit from including Constraints on Generalizability statements a proposed by Simons and colleagues (2017), but mainly because this improves falsifiability, not because it improves induction), and even if there is the tendency to overclaim in articles, I do not think this leads to an inferential crisis. Previous authors have made many of the same points, but in a more pragmatic manner (e.g., Barr et al., 2013m Clark, 1974,). Yarkoni fails to provide any insights into where the balance between generalizing to everything, and generalizing to factors that matter, should lie, nor does he provide an evaluation of how far off this balance research areas are. It is easy to argue any specific approach to science will not work in theory – but it is much more difficult to convincingly argue it does not work in practice. Until Yarkoni does the latter convincingly, I don’t think the generalizability crisis as he sketches it is something that will keep me up at night. <br />
<br />
<br />
<br />
<h4>
References</h4>
<br />
Baribault, B., Donkin, C., Little, D. R., Trueblood, J. S., Oravecz, Z., Ravenzwaaij, D. van, White, C. N., Boeck, P. D., & Vandekerckhove, J. (2018). Metastudies for robust tests of theory. Proceedings of the National Academy of Sciences, 115(11), 2607–2612. https://doi.org/10.1073/pnas.1708285114 <br />
<br />
Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3). https://doi.org/10.1016/j.jml.2012.11.001 <br />
<br />
Box, G. E. (1976). Science and statistics. Journal of the American Statistical Association, 71(356), 791–799. https://doi.org/10/gdm28w <br />
<br />
Clark, H. H. (1969). Linguistic processes in deductive reasoning. Psychological Review, 76(4), 387–404. https://doi.org/10.1037/h0027578 <br />
<br />
Cornfield, J., & Tukey, J. W. (1956). Average Values of Mean Squares in Factorials. The Annals of Mathematical Statistics, 27(4), 907–949. https://doi.org/10.1214/aoms/1177728067 <br />
<br />
Cortina, J. M., & Dunlap, W. P. (1997). On the logic and purpose of significance testing. Psychological Methods, 2(2), 161. <br />
<br />
Dienes, Z. (2008). Understanding psychology as a science: An introduction to scientific and statistical inference. Palgrave Macmillan. <br />
<br />
Fujisaki, W., & Nishida, S. (2009). Audio–tactile superiority over visuo–tactile and audio–visual combinations in the temporal resolution of synchrony perception. Experimental Brain Research, 198(2), 245–259. https://doi.org/10.1007/s00221-009-1870-x <br />
<br />
Hacking, I. (1965). Logic of Statistical Inference. Cambridge University Press. <br />
<br />
Henrich, J., Heine, S. J., & Norenzayan, A. (2010). Most people are not WEIRD. Nature, 466(7302), 29–29. <br />
<br />
Lakens, D. (2020). The Value of Preregistration for Psychological Science: A Conceptual Analysis. Japanese Psychological Review. https://doi.org/10.31234/osf.io/jbh4w <br />
<br />
Munafò, M. R., & Smith, G. D. (2018). Robust research needs many lines of evidence. Nature, 553(7689), 399–401. https://doi.org/10.1038/d41586-018-01023-3 <br />
<br />
Orben, A., & Lakens, D. (2019). Crud (Re)defined. https://doi.org/10.31234/osf.io/96dpy <br />
<br />
Simons, D. J., Shoda, Y., & Lindsay, D. S. (2017). Constraints on Generality (COG): A Proposed Addition to All Empirical Papers. Perspectives on Psychological Science, 12(6), 1123–1128. https://doi.org/10.1177/1745691617708630
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Daniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.com3