Bayesian vs Frequentist A/B Testing: Which Should You Use?

Bayesian versus frequentist is the closest thing A/B testing has to a holy war, and most of the shots fired miss the practical point: they are different questions, not different answers. This guide lays out what each framework actually computes and when each one earns its keep.

Two questions about the same data

The frequentist asks: if nothing were different, how surprising is this data? The answer is a p-value, plus a confidence interval built from a procedure guaranteed to cover the truth 95% of the time. The Bayesian asks: given this data (and my prior), what do I now believe about the true rates? The answers are direct probabilities — "there is a 96% chance B beats A" — plus credible intervals and, crucially, expected loss: how much conversion you sacrifice on average if you ship the variant and turn out wrong.

Where frequentist wins

P-values are the lingua franca of experimentation programs: auditable, convention-backed, and free of arguments about priors. Error-rate guarantees ("at most 5% of our shipped 'winners' are false") are program-level properties that compound well across hundreds of tests. If your organization runs a large testing program with centralized review, frequentist defaults plus pre-committed sample sizes (see sizing) are hard to beat.

Where Bayesian wins

Decisions, especially small-sample ones. "p = 0.11, fail to reject" leaves a PM nowhere; "91% chance it's better, and if we're wrong we lose at most 0.05% conversion" is a decision brief. Expected loss lets you ship promising variants early when the downside is provably tiny — a decision-theoretic move frequentist tests simply do not offer. Informative priors also let repeat experimenters stabilize noisy early data. Try it on the Bayesian calculator, which reports probability-to-be-best, expected loss, and credible intervals side by side.

The myths

"Bayesian tests let you peek freely." Mostly overstated: aggressive optional stopping on probability thresholds still inflates real-world error rates, even if the posterior remains coherent. If you want statistically safe peeking, that is precisely what sequential testing is for. "Priors are cheating." The uniform Beta(1,1) prior adds nothing to the data; informative priors are only as biased as the data behind them. "They disagree all the time." With flat priors and decent samples, both frameworks nearly always point the same direction; the difference is the vocabulary of the answer.

A practical default

Run both — they are free. Use the significance calculator for the record and the program-level error guarantees, and the Bayesian view for the shipping decision and the stakeholder conversation. When they agree (usually), decide with confidence. When they disagree, your test is underpowered — which the power calculator will confirm.