If you’ve ever shipped a feature and thought, “Did we actually make things better?”, you’re not alone. A/B testing is supposed to be our scientific answer to that question, but running good experiments takes more than sprinkling some feature flags and plotting a graph.
In practice, many teams learn experimentation the hard way. They launch tests with unclear hypotheses, biased assignments, or underpowered sample sizes, only to discover weeks later that their results are inconclusive or misleading. This means going back to the drawing board, restarting experiments, and losing valuable time, a hit to both product velocity and team morale.
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