Bayesian Statistics in Excel: Priors, Posteriors & Bayesian A/B Tests
Bayes' theorem, step-by-step updating, grid posteriors, Beta-Binomial, prior sensitivity, P(B > A) and expected loss: 8 hands-on labs with an online plant shop.
The first lesson is free — no account needed.
About this course
A Bayesian statistics course that builds every update in a spreadsheet on one online plant shop's data, for analysts, marketers and students who know a little probability. You start with Bayes' theorem on a diagnostic test and see why the base rate decides what a positive result means, update one batch through three pieces of evidence step by step and with odds and likelihood ratios, approximate a posterior on a grid with BINOM.DIST as the likelihood, apply the Beta-Binomial conjugate update with a 95% credible interval, compare a flat and a strong prior on the same 40 visitors, turn an A/B checkout test into the probability that B beats A, decide with expected loss and set the Bayesian answer next to a frequentist confidence interval and p-value for the same data. The first lab is free.
Lessons
- Bayes' Theorem: A Diagnostic Test and the Base RateFree
- Updating Step by Step: Several Pieces of Evidence Pro
- Grid Approximation: Prior × Likelihood ÷ Sum Pro
- Beta-Binomial: The Conjugate Update and a Credible Interval Pro
- Weak vs Strong Priors: How Much Does the Prior Matter? Pro
- Bayesian A/B Test: The Probability That B Beats A Pro
- Expected Loss: A Decision Rule for Shipping B Pro
- Bayesian vs Frequentist: Same Data, Different Questions Pro