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Browse 3 real-world technical and behavioral interview questions about Statistical power. Review scenarios, edge cases, and architectural best practices.
Fix the hypothesis, one primary metric, the randomisation unit and the sample size before launch. Peeking at a fixed-horizon test inflates false positives because each look is another chance to cross the threshold, and an inconclusive result is an interval to interpret, not proof of no effect.
No, if the analysis was designed as a fixed-horizon test: checking repeatedly inflates the false-positive rate, and stopping at the first significant look also overstates the effect size. Either commit to the horizon, or adopt a sequential design that budgets error across looks.
Establish whether the test could ever have detected the effect you cared about, because an underpowered null tells you nothing; then read the confidence interval rather than the verdict and decide whether to extend, test a larger change, or accept that the assumption was wrong.