Bayesian A/B Testing for E-Commerce
[Python][PyMC][Bayesian Inference][Monte Carlo]
§1. Abstract
When a website tests a new design (A/B testing), they usually use confusing math (p-values) to guess if it worked. I used a more advanced method (Bayesian statistics) that gives a straight answer: 'There is a 97% chance the new design is better, and it will make us an extra $36,000 a year.'
§2. Methodology & Implementation
Moved beyond traditional frequentist p-values by applying Bayesian inference to an e-commerce A/B test. Built a full pipeline using PyMC: specifying informative priors from historical data, computing posterior distributions analytically via conjugacy, and running Monte Carlo simulations to estimate the probability that the new design wins (97.2%) and compute expected revenue lift.
§3. Key Metrics
| Method | Bayesian Inference |
|---|---|
| P(B > A) | 97.2% |
| Exp. Lift | +$36k/yr |