This is the kind of question where you need to talk through hypothesis testing pretty carefully.
Start by framing the problem as a hypothesis test comparing conversion rates between control and treatment groups. Explain how to calculate statistical significance using appropriate tests (e.g., two-proportion z-test) and discuss practical significance by considering effect size and business impact. Emphasize the importance of power analysis and avoiding common pitfalls like peeking.
Pro tip: Always mention that statistical significance does not imply practical significance; a tiny lift can be statistically significant with huge sample sizes but may not be worth implementing. Also, highlight the need to correct for multiple comparisons if testing many variants.
Clearly state the null and alternative hypotheses (e.g., H0: p_control = p_treatment, H1: p_control ≠ p_treatment) and define the primary conversion metric.
Select a test like the two-proportion z-test or Fisher's exact test, ensuring assumptions (independence, sample size) are met.
Compute the z-score or chi-square statistic and derive the p-value to determine if the observed difference is statistically significant.
Evaluate the effect size (absolute and relative lift) and confidence intervals to judge if the difference is meaningful for the business.
Discuss how power analysis ensures adequate sample size to detect a meaningful effect, and warn against peeking or early stopping.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.