Courses/Behavioral Research: Statistical Methods

Behavioral Research: Statistical Methods

CG3.402

Vinoo Alluri•Monsoon 2025-26•4 credits

Revision Notes/Unit 13 — Bayesian Statistics

Unit 13 — Bayesian Statistics

Bayes' theorem with prior, likelihood, posterior; Bayes Factor and its interpretation (3–10 moderate · 10–30 strong · >30 very strong); BayesFactor R package; Bayesian advantages (evidence for null, optional stopping, priors).

Priors, Posteriors, Bayes Factors

12 min

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.