Bayesian Calibration Of P-Values From Fisher'S Exact Test

p-Values are commonly transformed to lower bounds on Bayes factors, so-called minimum Bayes factors. For the linear model, a sample-size adjusted minimum Bayes factor over the class of g-priors on the regression coefficients has recently been proposed (Held & Ott, The American Statistician 70(4), 335-341, 2016). Here, we extend this methodology to a logistic regression to obtain a sample-size adjusted minimum Bayes factor for 2 x 2 contingency tables. We then study the relationship between this minimum Bayes factor and two-sided p-values from Fisher's exact test, as well as less conservative alternatives, with a novel parametric regression approach. It turns out that for all p-values considered, the maximal evidence against the point null hypothesis is inversely related to the sample size. The same qualitative relationship is observed for minimum Bayes factors over the more general class of symmetric prior distributions. For the p-values from Fisher's exact test, the minimum Bayes factors do on average not tend to the large-sample bound as the sample size becomes large, but for the less conservative alternatives, the large-sample behaviour is as expected.