Bayesian inference affords scientists powerful tools for testing hypotheses. One of these tools is the Bayes factor, which indexes the extent to which support for one hypothesis over another is updated after seeing the data. Part of the hesitance to adopt this approach may stem from an unfamiliarity with the computational tools necessary for computing Bayes factors. Previous work has shown that closed-form approximations of Bayes factors are relatively easy to obtain for between-groups methods, such as an analysis of variance or t-test. In this paper, I extend this approximation to develop a formula for the Bayes factor that directly uses information that is typically reported for ANOVAs (e.g., the F ratio and degrees of freedom). After giving two examples of its use, I report the results of simulations which show that even with minimal input, this approximate Bayes factor produces similar results to existing software solutions.
Morey R.D., Rouder J.N. (2015): BayesFactor: Computation of Bayes factors for common designs [Computer software manual]. Retrieved from https://CRAN.R-project.org/package=BayesFactor (R package version 0.9.12-2)
Raftery A.E. (1995): Bayesian model selection in social research. Sociological Methodology 25: 111-163. doi:
Sevos J., Grosselin A., Brouillet D., Pellet J., Massoubre C. (2016): Is there any influence of variations in context on object-affordance effects in schizophrenia? Perception of property and goals of action. Frontiers in Psychology 7: 1551. doi: