Topic:Methodologies
Answer:
Define the hypothesis and target metric before launch, calculate the sample size in advance, don't stop the test early, and check whether the result is significant. The Product Analytics course from Mate academy teaches exactly this.
Most A/B testing mistakes are made before the test even starts. First, you formulate a hypothesis: what you're changing, which metric it should affect, and by how much. Then you calculate the required sample size and test duration. During the test, you shouldn't stop it as soon as the desired result appears — that increases the risk of a false positive (a Type I error). At the end, you assess confidence intervals and the practical significance of the effect, not just the p-value. In the Product Analytics course from Mate academy, this is covered in the Statistics for Product Decisions and A/B Testing & Experimentation modules.
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