tag:blogger.com,1999:blog-987850932434001559.post8271087340412589158..comments2023-09-17T16:45:23.263+02:00Comments on The 20% Statistician: Preventing common misconceptions about Bayes FactorsDaniel Lakenshttp://www.blogger.com/profile/18143834258497875354noreply@blogger.comBlogger1125tag:blogger.com,1999:blog-987850932434001559.post-64581285022385631622023-04-13T17:51:13.710+02:002023-04-13T17:51:13.710+02:00Thanks for writing this - after reading a recent t...Thanks for writing this - after reading a recent tweet of yours, I thought the arguments behind that brief statement must be somewhere in your posts and materials, ooor maybe even there's a new post in the making, and here it comes I guess :)<br /><br />Two - hopefully not silly - questions came into mind.<br /><br />1: "The correct claim is that people should update their belief in the alternative hypothesis by a factor of 10." - Doesn't this updating factor require that the personal prior belief of the reader was the same as the prior specified in the analysis? So when I don't agree with (or don't understand) the priors, then I'm probably gonna have a hard time drawing a conclusion for myself... (...which could also apply to a corresponding frequentist analysis as well)<br /><br />2: "Giving up error control also means giving up claims about the presence or absence of effects." - This is a strong claim, and I think this was basically what the tweet I was referring to was about. You concentrated on Bayes Factors, but I wondered how this would apply to a Bayesian inferring, plotting and characterizing the full Bayesian posterior distribution and make claims and interpretations ba(ye)sed on that. For instance, seeing that moost of the posterior mass encompasses a range of effects of meaningful strength, then they would base their interpretation and further research or other decision based on that. Would you consider some formal "Bayesian error control" possible and necessary in this situation? I'm curious how you look at this from your point of view.<br /><br />(I unfortunately don't have deep experience with Bayesian methods yet, but I'd think that a) based on the posterior distribution and the specific hypothesis, readers might be able to consider the weight of the evidence for themselves, b) maybe in critical situations, the downstream consequences of dichotomous decisions could even be explicitly modeled in a Bayesian framework.)bknakkerhttps://www.blogger.com/profile/01161397177372505020noreply@blogger.com