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Bayesian inference affords scientists with powerful tools for testinghypotheses. One of these tools is the Bayes factor, which indexes the extent towhich support for one hypothesis over another is updated after seeing the data.Part of the hesitance to adopt this approach may stem from an unfamiliaritywith the computational tools necessary for computing Bayes factors. Previouswork has shown that closed form approximations of Bayes factors are relativelyeasy to obtain for between- groups methods, such as an analysis of variance ort-test. In this paper, I extend this approximation to develop a formula for theBayes factor that directly uses information that is typically reported forANOVAs (e.g., the F ratio and degrees of freedom). After giving two examples ofits use, I report the results of simulations which show that even with minimalinput, this approximate Bayes factor produces similar results to existingsoftware solutions.

Computing Bayes factors to measure evidence from experiments: An extension of the BIC approximation