Bayesian Analysis for Applied Researchers
Bayesian Analysis
for Applied Researchers
A practical, scenario-based workflow for moving from p-values to posterior reasoning — priors, regression, multilevel models, model checking, and reporting.
Built for researchers who already use frequentist methods. Each module pairs a research scenario with the modeling decisions an applied analyst actually faces: which likelihood, which prior, which check, and which sentence to write when the posterior comes back.
Moeketsi Mosia
Guiding you through a practical workflow from modeling decisions to reporting claims.
A progression from p-values to posterior reporting
Three shifts that change what you can actually claim.
From p-values to posterior probabilities
A p-value tells you the data is surprising under a null you didn't believe. A posterior tells you what to believe now — and how strongly.
From default assumptions to explicit priors
Every analysis carries assumptions. Bayesian analysis asks you to write them down — and gives you the tools to stress-test them before they touch the data.
From coefficients to defensible research claims
A point estimate compresses everything into two numbers. A posterior keeps the shape — so reporting keeps the nuance reviewers and stakeholders are looking for.