This chapter gives a brief overview of Bayesian hypothesis testing. We first describe a standard Bayesian analysis of a single binomial response, going through the prior distribution choice and explaining how the posterior is calculated. We then discuss Bayesian hypothesis testing using the Bayes factor, a measure of how much the posterior odds of believing in one hypothesis changes from the prior odds. We show, using a binomial example, how the Bayes factor may be highly dependent on the prior distribution, even with extremely large sample sizes. We next discuss Bayes hypothesis testing using decision theory, reviewing the intrinsic discrepancy of Bernardo, as well as the loss functions proposed by Freedman. Freedman’s loss functions allow the posterior belief in the null hypothesis to equal the p-value. We next discuss well-calibrated null preferences priors, which applied to parameters from the natural exponential family (binomial, negative binomial, Poisson, normal), also give the posterior belief in the null hypothesis equal to valid one-sided p-values, and give credible intervals equal to valid confidence intervals.