Bayesian methods apply probability theory in a way that continuously refines predictions based on incoming data. Unlike classical statistics, which rely on fixed assumptions, Bayesian analysis adapts as new information emerges. In A/B testing, this approach can show the likelihood of one variation outperforming another in real time, without waiting for a fixed sample size. It gives decision-makers faster, more flexible insights into experiments.
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