Hypothesis Testing

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Hypothesis testing is a structured way to check whether a change truly made a difference or whether the result could be explained by random variation. It starts with a default assumption that nothing changed, then compares outcomes after the change to see how likely the observed gap would be if that assumption were true. When that likelihood is very low, teams treat the result as strong enough evidence to act on.

Teams use hypothesis testing in A/B experiments and before–after studies. Good practice starts with deciding which metric matters most and what level of false alarms is acceptable. It also means collecting enough data to spot a meaningful change, and resisting the urge to stop early just because the chart looks good today. The point is to make a confident ship or no-ship decision while being honest about uncertainty.

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