  Bayesian inference affords scientists with 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.

Computation;Methodology