Bayesian A/B Testing: What It Is and How It Works

Bayesian A/B testing uses Bayes’ theorem to combine prior beliefs about conversion rates with observed results, producing posterior distributions that estimate the probability a variant outperforms a control and the likely size of the effect through credible intervals. Unlike frequentist testing, which relies on p-values, fixed significance thresholds, and planned sample sizes, Bayesian testing allows results to be viewed throughout an experiment without the same false-positive inflation from repeated checks. For binary conversion data, beta priors and binomial likelihoods commonly provide efficient posterior calculations, while more complex cases may require simulation methods such as Markov Chain Monte Carlo. In an onboarding example where one variant converts at 13% versus a control’s 11%, the method estimates roughly a 90% probability that the variant is better, although its credible interval still includes the possibility of no improvement. Bayesian methods can make results easier to communicate, incorporate relevant historical knowledge, and support feature-flag rollouts that ramp winning variants or reverse losing ones as evidence develops, but informative priors can bias results if poorly chosen, computations can be more demanding, and stakeholders may be less familiar with the terminology. Flagsmith’s beta Experimentation feature is presented as using a Bayesian engine to report lift, credible intervals, and each variant’s chance of beating the control.

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