##### Scenario Product team wants to launch a new recommendation algorithm and asks for a rigorous A/B test plan. ##### Question Design an A/B test to measure the uplift of the new recommender on GMV. Which primary and guardrail metrics will you track and why? Compute required sample size assuming 3% baseline conversion, 7% relative lift, α=0.05, power=0.8. How would you address novelty effect and uneven seasonality across groups? Explain how you would interpret results if the primary metric is flat but secondary engagement metrics improve. ##### Hints Talk randomization, CUPED, sequential testing, and post-test segmentation.