bayesian-ab-testing
Bayesian A/B Testing with PyMC
For comparing two variants on a conversion metric, use Bayesian A/B testing. It directly answers the questions stakeholders actually ask — "What's the probability B is better?" and "How much do we lose if we're wrong?" — without the fragile rituals of frequentist hypothesis testing (no p-values, no fixed sample sizes, no "don't peek" rules).
When to use this skill
- Two variants (A/B) with a binary outcome (converted / didn't)
- You want to know P(B > A) and expected loss, not a p-value
- You want to monitor the experiment continuously without inflating error rates (Bayesian posteriors are always valid — peeking is free)
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