Behavioral Research: Statistical Methods

Frequentist statistics treats probability as long-run frequency; parameters are fixed unknowns. Bayesian statistics treats probability as **degree of belief** in propositions — and any belief gets *updated* by evidence. The engine is one equation: posterior = (likelihood × prior) / evidence. Once you internalise that any data analysis is a belief update, every familiar test becomes a special case. The Bayes Factor — ratio of how well two hypotheses predict the data — is the Bayesian counterpart of the p-value, but with two advantages: it can support the null, and it doesn't care if you peeked at your data.