Estimating Evidential Value From Analysis of Variance Summaries: A Comment on Ly et al. (2018)

The past decade has witnessed a tremendous increase in the number of tools that enable social-science researchers to perform Bayesian inference. Ly et al. (2018) recently described one such tool: the Summary Stats module included as part of the open-source software package JASP (JASP Team, 2018). With this tool, researchers can input a minimal set of summary statistics (either from their own previously run analysis or from the published results of other researchers) and obtain a Bayesian reevaluation of the results. In particular, the Summary Stats module reports the Bayes factor (Kass & Raftery, 1995), a continuous index of the extent to which observed data are more likely under one hypothesis than under another, competing hypothesis. For example, the Bayes factor BF₀₁ describes the factor by which one’s prior belief about the relative likelihood of the null hypothesis H₀ over the alternative hypothesis H₁ should be updated after one observes data. This characterization makes the Bayes factor a useful measure of the evidential value of data (Etz & Vandekerckhove, 2017).