Bayesian A/B testing estimates the probability that a variant outperforms the control and expresses uncertainty as a credible interval instead of relying on a single lift number.
Chance to beat control
This probability answers a direct decision question, but it does not tell you whether the likely improvement is large enough to justify rollout.
Credible intervals
The interval shows a plausible range for the effect. Wide intervals warn that the estimate is still uncertain even when the displayed lift looks attractive.
Make a business decision
Combine probability, effect size, downside risk, runtime, traffic mix, and implementation cost. The strongest statistical variant is not always the best operational choice.
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