*In a previous post, I compared equivalence tests to Bayes factors, and pointed out several benefits of equivalence tests. But a much more logical comparison, and one I did not give enough attention to so far, is the ROPE procedure using Bayesian estimation. I’d like to thank John Kruschke for feedback on a draft of this blog post. Check out his own recent blog comparing ROPE to Bayes factors here.*

When we perform a study, we would like to conclude there is an effect, when there is an effect. But it is just as important to be able to conclude there is no effect, when there is no effect. I’ve recently published a paper that makes Frequentist equivalence tests (using the two-one-sided tests, or TOST, approach) as easy as possible (Lakens, 2017). Equivalence tests allow you to reject the presence of any effect you care about. In Bayesian estimation, one way to argue for the absence of a meaningful effect is the Region of Practical Equivalence (ROPE) procedure (Kruschke, 2014, chapter 12), which is “somewhat analogous to frequentist equivalence testing” (Kruschke & Liddell, 2017).

In the ROPE procedure, a 95% Highest Density Interval (HDI)
is calculated based on a posterior distribution (which is calculated based on a
prior and the data). Kruschke suggests that: “

*if the 95 % HDI falls entirely inside the ROPE then we decide to accept the ROPE’d value for practical purposes*”. Note that the same HDI can also be used to reject the null hypothesis, where in Frequentist statistics, even though the confidence interval plays a similar role, you would still perform both a traditional*t*-test and the TOST procedure.
The only real difference with equivalence testing is that
instead of using a confidence interval, a Bayesian Highest Density Interval is
used. If the prior used by Kruschke was perfectly uniform, ROPE and equivalence
testing would identical, barring philosophical differences in how the numbers
should be interpreted. The BEST package by default uses a ‘broad’ prior, and
therefore the 95% CI and 95% HDI are not exactly the same, but they are very
close, for single comparisons. When multiple comparisons are made, (for example
when using sequential analyses, Lakens, 2014), the CI needs to be adjusted to maintain the
desired error rate, but in Bayesian statistics, error rates are not directly
controlled (they are limited due to ‘shrinkage’, but can be inflated beyond 5%,
and often considerably so).

In the code below, I randomly generate random normally distributed data (with means of 0 and a sd of 1) and perform the ROPE procedure and the TOST. The 95% HDI is from -0.10 to 0.42, and the 95% CI is from -0.11 to 0.41, with mean differences of 0.17 or 0.15.

In the code below, I randomly generate random normally distributed data (with means of 0 and a sd of 1) and perform the ROPE procedure and the TOST. The 95% HDI is from -0.10 to 0.42, and the 95% CI is from -0.11 to 0.41, with mean differences of 0.17 or 0.15.

Indeed, if you will forgive me the pun, you might say these
two approaches are practically equivalent. But there are some subtle
differences between ROPE and TOST

**95% HDI vs 90% CI**

Kruschke (2014,
Chapter 5) writes: “

*How should we define “reasonably credible”? One way is by saying that any points within the 95% HDI are reasonably credible.*” There is not a strong justification for the use of a 95% HDI over a 96% of 93% HDI, except that it mirrors the familiar use of a 95% CI in Frequentist statistics. In Frequentist statistics, the 95% confidence interval is directly related to the 5% alpha level that is commonly deemed acceptable for a maximum Type 1 error rate (even though this alpha level is in itself a convention without strong justification).
But here’s the catch: The TOST equivalence testing procedure
does not use a 95% CI, but a 90% CI. The reason for this is that two one-sided
tests are performed. Each of these tests has a 5% error rate. You might
intuitively think that doing two tests with a 5% error rate will increase the
overall Type 1 error rate, but in this case, that’s not true. You could easily
replace the two tests, with just one test, testing the observed effect against
the equivalence bound (upper or lower) closest to it. If this test is
statistically significant, so is the other – and thus, there is no alpha
inflation in this specific case. That’s why the TOST procedure uses a 90% CI to
have a 5% error rate, while the same researcher would use a 95% CI in a
traditional two-sided

*t*-test to examine whether the observed effect is statistically different from 0, while maintaining a 5% error rate (see also Senn, 2007, section 22.2.4)
This nicely illustrates the difference between estimation
(where you just want to have a certain level of accuracy, such as 95%), and
Frequentist hypothesis testing, where you want to distinguish between signal
and noise, and not be wrong more than 5% of the time when you declare there is
a signal. ROPE keeps the accuracy the same across tests, Frequentist approaches
keep the error rate constant. From a Frequentist perspective, ROPE is more
conservative than TOST, like the use of alpha = 0.025 is more conservative than
the use of alpha = 0.05.

**Power analysis**

For an equivalence test, power analysis can be performed
based on closed functions, and the calculations take just a fraction of a
second. I find that useful, for example in my role in our ethics board, where
we evaluate proposals that have to justify their sample size, and we often
check power calculations. Kruschke has an excellent R package (BEST) that can
do power analyses for the ROPE procedure. This is great work – but the
simulations take a while (a little bit over an hour for 1000 simulations).

Because the BESTpower function relies on simulations, you
need to specify the sample size, and it will calculate the power. That’s
actually the reverse of what you typically want in a power analysis (you want
to input the desired power, and see which sample size you need). This means you
most likely need to run multiple simulations in BESTpower, before you have
determined the sample size that will yield good power. Furthermore, the
software requires your to specify the expected means and standard deviations,
instead of simply an expected effect size. Instead of Frequentist power
analysis, where the hypothesized effect size is a point value (e.g., d = 0.4),
Bayesian power analysis models the alternative as a distribution, acknowledging
there is uncertainty.

In the end, however, the result of a power analysis for ROPE
and for TOST is actually remarkably similar. Using the code below to perform
the power analysis for ROPE, we see that 100 participants in each group give us
approximately 88.4% power (with 2000 simulations, this estimate is still a bit
uncertain) to get a 95% HDI that falls within our ROPE of -0.5 to 0.5, assuming
standard deviations of 1.

We can use the powerTOSTtwo.raw function in the TOSTER
package (using an alpha of 0.025 instead of 0.05, to mirror to 95% HDI) to
calculate the sample size we would need to achieve 88.4% power for independent
t-test (using equivalence bounds of -0.5 and 0.5, and standard deviations of
1):

**powerTOSTtwo.raw(alpha=0.025,statistical_power=0.875,low_eqbound=-0.5,high_eqbound=0.5,sdpooled=1)**

The outcome is 100 as well. So if you use a broad prior, it
seems you can save yourself some time by using the power analysis for
equivalence tests, without severe consequences.

**Use of prior information**

The biggest benefit of ROPE over TOST is that is allows you
to incorporate prior information in your data analysis. If you have reliable
prior information, ROPE can use this information, which is especially useful if
you don’t have a lot of data. If you use priors, it is typically advised to
check the robustness of the posterior against reasonable changes in the prior (Kruschke, 2013).

**Conclusion**

Using the ROPE procedure or the TOST procedure will most
likely lead to very similar inferences. For all practical purposes, the
differences are small. It’s quite a lot easier to perform a power analysis for
TOST, and by default, TOST has greater statistical power because it uses 90% CI.
But power analysis is possible for ROPE (which is a rare pleasure to see for
Bayesian analyses), and you could choose to use a 90% HDI, or any other value
that matches your goals. TOST will be easier and more familiar because it is
just a twist on the classic

*t*-test, but ROPE might be a great way to dip your toes in Bayesian waters and explore the many more things you can do with Bayesian posterior distributions.**References**

Kruschke, J. (2013). Bayesian estimation supersedes the t test.

*Journal of Experimental Psychology: General*,

*142*(2), 573–603. https://doi.org/10.1037/a0029146

Kruschke, J. (2014).

*Doing Bayesian Data Analysis, Second Edition: A Tutorial with R, JAGS, and Stan*(2 edition). Boston: Academic Press.
Kruschke, J., & Liddell, T. M.
(2017). The Bayesian New Statistics: Hypothesis testing, estimation,
meta-analysis, and power analysis from a Bayesian perspective.

*Psychonomic Bulletin & Review*. https://doi.org/10.3758/s13423-016-1221-4
Lakens, D. (2014). Performing
high-powered studies efficiently with sequential analyses: Sequential analyses.

*European Journal of Social Psychology*,*44*(7), 701–710. https://doi.org/10.1002/ejsp.2023
Lakens, D. (2017). Equivalence tests: A
practical primer for t-tests, correlations, and meta-analyses.

*Social Psychological and Personality Science*.
Senn, S. (2007).

*Statistical issues in drug development*(2nd ed). Chichester, England ; Hoboken, NJ: John Wiley & Sons.