--- title: Bayesian A/B Testing — definition | RGM® Glossary url: https://realgrowthmatters.com/glossary/bayesian-ab-testing/ updated: 2026-06-10 source_html: https://realgrowthmatters.com/glossary/bayesian-ab-testing/ --- # Bayesian A/B Testing Bayes·i·an A/B test·ing/ˈbeɪʒən ə bi ˈtɛstɪŋ/noun Instead of 'is this significant?' it answers 'what's the chance B beats A, and by how much?' — the question teams actually have. Term : Bayesian A/B Testing Reports : P(variant best), expected loss, credible intervals Contrast : Frequentist p-value / significance Strength : Intuitive outputs, graceful early reads ## Forms & parts of speech probability to beat · phrase The Bayesian headline output. "**Probability to beat** control is 96% with tiny expected loss — ship it." ## Definition in plain terms Bayesian A/B testing frames experiments as updating beliefs with evidence: starting from a prior, the data produces a posterior distribution, and the test reports intuitive quantities — the PROBABILITY that variant B beats A, the expected magnitude of the difference, and the 'expected loss' from choosing wrong. It answers the question teams actually ask ('what's the chance B is better, and by how much?') rather than the frequentist question ('could this data have arisen if there were no difference?'). ## The mechanics The practical contrasts with frequentist testing: Bayesian outputs are directly interpretable (a 95% probability-to-beat means what people wrongly think a p-value means), it handles early looks and ongoing monitoring more gracefully (the posterior just updates, with less of the rigid peeking penalty — though decision rules still matter), and 'expected loss' supports risk-based stopping. The honest caveats: the PRIOR is a real choice (a strong prior sways small samples — usually set weak/uninformative), Bayesian methods aren't immune to bias or underpowering (a confident-looking posterior on tiny data is still tiny data), and the framework is a different lens, not a license to skip rigor. Both schools, done well, converge. ## When it matters Bayesian testing fits teams that want decision-shaped outputs (probability and expected loss map cleanly onto 'ship or not'), continuous-monitoring contexts, and stakeholders who misread p-values (the Bayesian number means what they think it means). Frequentist methods remain the regulated-research default and many platforms' native mode. The mature stance is method-agnostic: the discipline — adequate data, honest priors or honest alpha, pre-set decision rules, effect sizes that matter — outranks the school. Pick the lens whose outputs your team will read correctly. **Worked example.** A growth team keeps misreading p-values — treating 'p = 0.05' as '95% likely to be real' and shipping noise. Switching the testing tool to a Bayesian readout aligns the output with the decision: tests now report 'probability B beats A' and 'expected loss if you ship B and you're wrong.' The team sets a clear rule (ship at 95% probability-to-beat AND expected loss below a threshold), with weak priors so small samples can't be swayed by assumption. Decision quality rises — not because Bayesian is magic, but because the number finally means what the team always thought it meant. **Failure modes to watch.** Using a strong prior that sways small-sample results; treating a confident posterior on thin data as conclusive; assuming Bayesian methods exempt you from adequate sample sizes; and switching schools to escape rigor rather than to fit the decision. ## Synonyms & antonyms ### Synonyms Bayesian A/B testingBayesian experimentationprobability-to-beat testing ### Antonyms frequentist testingp-value significance testing ## Origin & history Bayesian inference descends from Thomas Bayes' 18th-century theorem (published posthumously, 1763); its application to online A/B testing was popularized in the 2010s by experimentation platforms (VWO's Bayesian engine, 2015) and writers seeking more interpretable, peeking-tolerant alternatives to frequentist significance. Etymology: [source](https://en.wikipedia.org/wiki/Bayesian_inference). ## Usage trends Search interest for this term over the last five years: [View interest-over-time on Google Trends →](https://trends.google.com/trends/explore?q=bayesian%20ab%20testing&date=today%205-y) ## Common questions What is Bayesian A/B testing? : An approach reporting the probability one variant beats another and the expected cost of choosing wrong — updating beliefs with data. How does it differ from frequentist testing? : It outputs directly interpretable probabilities and expected loss, rather than p-values and significance against a null hypothesis. Is Bayesian testing better? : Different, not automatically better — its outputs are more intuitive, but it still needs adequate data, honest priors, and pre-set decision rules. ## Related tools & calculators - tool[Experiment planner](/tools/experiment-planner/) - tool[Funnel drop-off analyzer](/tools/funnel-drop-off-analyzer/) ## Resources & people to follow - book*Trustworthy Online Controlled Experiments* — Kohavi, Tang & Xu - referenceVWO / Dynamic Yield — Bayesian engine documentation - referenceRGM analysis — method-agnostic rigor beats school loyalty Curated, non-competitor resources verified per term. ## Related training - module[CRO & experimentation](/training/cro-experimentation/) ## Disciplines Areas of marketing where bayesian a/b testing is a core concern: [Experimentation](/training/cro-experimentation/)[Analytics](/training/marketing-analytics/) ## Read next ## Related terms [A/B test](/glossary/ab-test/)[Statistical significance](/glossary/statistical-significance/)[P-value](/glossary/p-value/)[Sequential testing](/glossary/sequential-testing/)[Nudge (podcast)](/glossary/nudge-podcast/) ## Sources 1. trends[Google Trends — "bayesian ab testing"](https://trends.google.com/trends/explore?q=bayesian%20ab%20testing&date=today%205-y)