Statistical significance evaluates how surprising observed data would be under a null model. Bayesian probability estimates how likely one variant is to outperform another under the model and prior assumptions.
What a p-value says
A p-value is not the probability that the variant is better. It measures how compatible the observed result is with a specified null model when the analysis plan is followed.
What Bayesian probability says
Chance to beat control directly expresses uncertainty about which variant performs better. It should be read with the estimated effect and credible interval, not as a standalone badge.
Both need decision rules
Repeated peeking, changing metrics, and testing many variants can produce misleading confidence in either framework. Define the analysis, minimum runtime, and practical effect threshold before launch.
Business value remains separate
A highly probable improvement can still be too small to matter, and a promising estimate can remain too uncertain to ship. Combine statistical evidence with cost, risk, reversibility, and expected value.
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