A/B testing is an essential tool for making data-driven decisions, but selecting the right statistical method is crucial to obtaining accurate results. Depending on the data scenario, you can apply different tests: a t-test for comparing a sample to a population or two independent numerical variables with a normal distribution; a U-test (Mann-Whitney) for two independent numerical variables with a non-normal distribution; a paired t-test for evaluating two dependent numerical variables; and ANOVA for comparing one numerical and one categorical independent variable with a normal distribution. By choosing the correct approach based on the nature of your data, you can ensure your A/B tests provide meaningful insights that drive informed decision-making.
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