Prof. Dr. Felix Schönbrodt
by Angelika Stefan & Felix Schönbrodt This is the second part of “Two meanings of priors”. The first part explained a first meaning – “priors as subjective probabilities of models”. While the first meaning of priors refers to a global Continue Reading …
by Angelika Stefan & Felix Schönbrodt When reading about Bayesian statistics, you regularly come across terms like “objective priors“, “prior odds”, “prior distribution”, and “normal prior”. However, it may not be intuitively clear that the meaning of “prior” differs in Continue Reading …
There are at least three traditions in statistics which work with a kind of likelihood ratios (LRs): the “Bayes factor camp”, the “AIC camp”, and the “likehood camp”. In my experience, unfortunately most people do not have an intuitive understanding Continue Reading …
A Bayes factor (BF) is a statistical index that quantifies the evidence for a hypothesis, compared to an alternative hypothesis (for introductions to Bayes factors, see here, here or here). Although the BF is a continuous measure of evidence, humans Continue Reading …
I want to present a re-analysis of the raw data from two studies that investigated whether physical cleanliness reduces the severity of moral judgments – from the original study (n = 40; Schnall, Benton, & Harvey, 2008), and from a Continue Reading …
The probably most frequent criticism of Bayesian statistics sounds something like “It’s all subjective – with the ‘right’ prior, you can get any result you want.”. In order to approach this criticism it has been suggested to do a sensitivity Continue Reading …
[Update Oct 2014: Due to some changes to the Bayes factor calculator webpage, and as I understand BFs much better now, this post has been updated …] I started to familiarize myself with Bayesian statistics. In this post I’ll show Continue Reading …
One critique frequently heard about Bayesian statistics is the subjectivity of the assumed prior distribution. If one is cherry-picking a prior, of course the posterior can be tweaked, especially when only few data points are at hand. For example, see Continue Reading …