The implementation of Bayesian ANOVA in JASP: A practical primer postprint

Abstract: The application of Bayesian statistics to hypothesis testing - Bayes factors - is increasing in psychological science. Bayes factors quantify the evidence supporting the competing hypothesis or model, respectively, thereby making a judgment about which hypothesis or model is more supported by the data based on its value. The principles and applications of Bayes factor for ANOVA are, however, not available in China. We first present the theoretical foundation of Bayesian ANOVA and its calculation rules. It also shows how to perform Bayesian ANOVA and how to interpret and report the results of five common designs (one-factor between-group design, one-factor within-group design, two-factor between-group design, two-factor within-group design, and two-factor mixed design) using example data. Theoretically, Bayesian ANOVA is an effective alternative to conventional ANOVA as a powerful vehicle for statistical inferences.