<b>"95% probability B beats A" and "p&lt;0.05" are not the same claim — and teams mix them up constantly.</b>

<b>"95% probability B beats A" and "p&lt;0.05" are not the same claim — and teams mix them up constantly.</b>
Bayesian gives you the probability the variant is better given the data. Frequentist p-values give you the probability of the data given no effect. Different questions.
— Bayesian shines for sequential looks and "expected loss" stopping rules.
— Neither rescues a tiny sample; both just quantify the uncertainty honestly.
Pick a framework, then read its number on its own terms.
Read the number, not the story. [P(B&gt;A) ≠ 1−p]