JASP is a free statistics program developed by the group of Eric-Jan Wagenmakers at the University of Amsterdam. JASP is great for doing traditional null hypothesis testing: the kind of statistics that gives you p values.
But JASP is not a traditional program. It's a program with a mission. And its mission is to convince scientists to do Bayesian Statistics: the kind of statistics that gives you Bayes Factors.
A lot has been been written about Bayesian statistics, and much of that by people more knowledgeable than me. So I'll stick to a basic description of what a Bayes Factor is. And then I'll dive right into how you can apply this knowledge to a specific, but very common, kind of statistical test: a Repeated Measures Analysis of Variance, often simply called a Repeated Measures.
For this post, I'll assume that you know what a Repeated Measures is.
What's a Bayes Factor?
A Bayes Factor reflects how likely data is to arise from one model, compared to another model. Typically, one of the models is the null model (H0): a model that predicts that your data is purely random noise. The other model then typically has one or more effects in it, so it's (one of) the alternative hypotheses that you want to test (HA). If the data is much more likely to arise under HA than under H0, this means that there is strong evidence in the data for HA. This, in a nutshell, is the logic behind the …