Standard methods for analyzing binomial regression data rely on asymptotic inferences. Bayesian methods apply for any sample size. We discuss Bayesian inferences for binomial regression with an emphasis on inferences for the probability of “success.” Furthermore, we illustrate diagnostic tools, perform model selection among non-nested models, and examine the sensitivity of the Bayesian methods. Computations are discussed in terms of a discrete approximation to the posterior distribution as is obtained from any Bayesian Monte Carlo method.