Overview
Frequentist hypothesis testing answers the question "What is the probability of observing data at least this extreme if H0 is true?" — which is not the question most researchers want to answer. Most researchers want to know "What is the probability that H1 is true given the data I observed?" This is a Bayesian question. The Bayes Factor quantifies how much the data changes the probability ratio between H1 and H0 — providing continuous evidence strength rather than binary significance thresholds.
The Bayesian Hypothesis Testing Framework specifies priors, calculates Bayes Factors, and produces posterior distributions that answer the question researchers actually want answered: how likely is each hypothesis given the observed evidence?
Key Features
Prior specification with justification
BF10 calculation with interpretation table
Posterior distribution with credible interval