A/B testing is the gold standard of experimentation. It is meant to help companies make faster, better, data-driven decisions. But too often, it does the opposite. The meeting starts with optimism: a new pricing idea, ad layout, or signup screen goes into an A/B test. After waiting for weeks, analysts come back with p-values, 95% confidence thresholds, and a familiar conclusion: “We should wait for more data. We don’t have enough evidence yet, and it’s not statistically significant.”
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