Hypothesis Testing in the Bayesian Framework

Abstract

While the Bayesian parameter estimation has gained a wider acknowledgement among political scientists, they seem to have less discussed the Bayesian version of hypothesis testing. This paper introduces two Bayesian approaches to hypothesis testing: one based on estimated posterior distributions and the other based on Bayes factors. By using an example based on a linear regression model, I demonstrate similarities and differences not only between the null-hypothesis significance tests and Bayesian hypothesis tests, but also those among two different Bayesian approaches, which are also critically discussed.