Introducing frequentist inference in Firebase A/B Testing

Firebase A/B Testing is replacing its Bayesian results methodology, previously powered by Google Optimize, with a frequentist approach for newly created experiments following Google Optimize’s sunset. The change is intended to make test results more transparent and independently verifiable by providing inputs such as variant outcomes, user counts, and standard deviations that can be used to calculate p-values and related statistics. Rather than waiting a fixed number of days to identify a leader, Firebase will report leading outcomes once detected, while encouraging adequate sample sizes and consideration of short-term effects. The updated results present statistical significance, confidence intervals, and changes in primary and secondary metrics, helping teams assess whether a variant improves outcomes such as purchase revenue without materially affecting retention or app stability. Existing experiments will continue using Bayesian inference through completion, and completed Bayesian experiments will retain their original results displays.

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