A tutorial on testing hypotheses using the Bayes factor

Learning about hypothesis evaluation using the Bayes factor could enhance psychologicalresearch. In contrast to null-hypothesis significance testing: it renders the evidence in favorof each of the hypotheses under consideration (it can be used to quantify support for thenull-hypothesis) instead of a dichotomous reject/do-not-reject decision; it canstraightforwardly be used for the evaluation of multiple hypotheses without having tobother about the proper manner to account for multiple testing; and, it allows continuousre-evaluation of hypotheses after additional data have been collected (Bayesian updating).This tutorial addresses researchers considering to evaluate their hypotheses by meansof the Bayes factor. The focus is completely applied and each topic discussed is illustratedusing Bayes factors for the evaluation of hypotheses in the context of an ANOVA model,obtained using the R package bain. Readers can execute all the analyses presented whilereading this tutorial if they download bain and the R-codes used. It will be elaborated in acompletely non-technical manner: what the Bayes factor is, how it can be obtained, howBayes factors should be interpreted, and what can be done with Bayes factors. Afterreading this tutorial and executing the associated code, researchers will be able to use theirown data for the evaluation of hypotheses by means of the Bayes factor, not only in thecontext of ANOVA models, but also in the context of other statistical models.