How to Design A/B Tests That Deliver Meaningful Business Insights

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A/B testing is a widely used method for comparing two versions of a product or feature, but many teams misinterpret its results. A core mistake is conflating statistical significance with business significance — a result can be statistically real yet practically irrelevant, as illustrated by Pepsi's 1970s blind taste tests that inadvertently triggered Coca-Cola's failed 'New Coke' launch. Every valid experiment requires a clearly defined hypothesis, random assignment of subjects to control and treatment groups, and a pre-set significance threshold to control false positive rates. The p-value, commonly misread as the probability that a change works, actually measures how often a result as extreme as observed would appear by chance if the change had no effect. Understanding error types, sample size requirements, and the difference between statistical and practical significance are essential for running tests that generate genuinely actionable findings.

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