A tutorial on conducting and interpreting a Bayesian ANOVA in JASP

By Don van den Bergh, Johnny van Doorn, Maarten Marsman, Tim Draws, Erik-Jan van Kesteren, Koen Derks, Fabian Dablander, Quentin F. Gronau, Šimon Kucharský, Akash R. Komarlu Narendra Gupta, Alexandra Sarafoglou, Jan G. Voelkel, Angelika Stefan, Alexander Ly, Max Hinne, Dora Matzke, Eric-Jan Wagenmakers

Analysis of variance (ANOVA) is the standard procedure for statistical inference in factorial designs. Typically, ANOVAs are executed using frequentist statistics, where p-values determine statistical significance in an all-or-none fashion. In recent years, the Bayesian approach to statistics is increasingly viewed as a legitimate alternative to the p-value. However, the broad adoption of Bayesian statistics—and Bayesian ANOVA in particular—is frustrated by the fact that Bayesian concepts are rarely taught in applied statistics courses. Consequently, practitioners may be unsure how to conduct a Bayesian ANOVA and interpret the results. Here we provide a guide for executing and interpreting a Bayesian ANOVA with JASP, an open-source statistical software program with a graphical user interface. We explain the key concepts of the Bayesian ANOVA using two empirical examples.

  • Bayes factor
  • analysis of variance
  • JASP
  • posterior distribution
  • hypothesis test
  • tutorial
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