A Review of Applications of the Bayes Factor in Psychological Research

The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: Bayesian evaluation of point null, interval, and informative hypotheses, Bayesian evidence synthesis, Bayesian variable selection and model averaging, and Bayesian evaluation of cognitive models. We elaborate what each application entails, give illustrative examples, and provide an overview of key references and software with links to other applications. The article is concluded with a discussion of the opportunities and pitfalls of Bayes factor applications and a sketch of corresponding future research lines. Translational Abstract The last 25 years have shown a steady increase in attention for the Bayes factor as a tool for hypothesis evaluation and model selection. The Bayes factor provides a method for quantifying the relative evidence for two competing hypotheses that are both instantiated by specific statistical models with prior distributions on the parameters. This general approach can be used to address many specific, theoretically relevant research questions. The present review highlights the potential of the Bayes factor in psychological research. We discuss six types of applications: whether a randomized experiment has an effect or not (point null hypothesis), whether an effect is inside or outside a range of negligible effect sizes (interval hypothesis), whether a set of means follows a specific order (informative hypothesis), whether a set of studies jointly corroborate a theoretical claim (evidence synthesis), which variables are most relevant for prediction (variable selection), and which model provides the best account of latent processes (cognitive modeling). We elaborate what each application entails, give illustrative examples with reproducible files for the software R and JASP, and provide an overview of key references and software with links to other applications. We concluded with a discussion of the opportunities and pitfalls of the Bayes factor.