Bayesian A/B Testing

Bayesian A/B testing frames experiment analysis in terms of probabilities rather than p-values. Instead of asking whether a result is statistically significant at the 0.05 level, Bayesian analysis asks what the probability is that variant B is better than variant A and by how much. It incorporates prior beliefs about expected effect sizes and updates them as data accumulates.

For growth teams, Bayesian methods provide more intuitive results that directly answer the questions decision-makers actually care about. AI enhances Bayesian experimentation through informative prior construction based on historical experiment results, automated expected loss calculations that quantify the risk of choosing each variant, and adaptive allocation that shifts traffic toward better-performing variants during the experiment. Growth engineers should consider Bayesian methods for their experimentation platform because the probability-based output, such as a 95% probability that variant B is 3-7% better, is more directly actionable than frequentist confidence intervals for non-statistician stakeholders. Key implementation considerations include choosing appropriate priors that reflect genuine prior knowledge without biasing results, and computing posterior distributions efficiently for large-scale experimentation. Teams should standardize their Bayesian decision criteria, such as requiring a 95% probability of positive effect and an expected loss below a defined threshold, before launching experiments.