A Simple Method for Teaching Bayesian Hypothesis Testing in the Brain and Behavioral Sciences

Abstract

Undergraduate statistics courses in the brain and behavioral sciences tend to be well-grounded in classical null hypothesis significance testing. However, many journals in the fields of neuroscience and psychology are turning away from these classical methods and their reliance on p-values in favor of alternative methods. One such alternative is Bayesian inference, and in particular, the Bayes factor, which indexes the extent to which observed data supports one hypothesis over another. As such, the Bayes factor provides an easy-to-interpret measure of evidence. However, this ease of interpretation is often in stark contrast with the actual ease of computation, even for simple experimental designs. In this paper, I present an easy-to-use formula for computing Bayes factors for two common hypothesis testing situations: the one-way ANOVA and the independent samples t-test. I give examples of its use and recommendations of how to report the results, which should help any teacher of statistics and research methods begin to incorporate Bayesian statistics into quantitative methods courses.

Keywords: Bayes factor; Bayesian methods; Hypothesis testing; Statistical inference; statistics education.