This question is basically ten questions stapled together.
Structure your answer around the experiment lifecycle: start with a clear hypothesis and metric definition, then detail the randomization unit and variance reduction techniques, followed by sample size calculation and risk mitigation. Emphasize causal inference and practical considerations specific to a marketplace search context.
Pro tip: Mention that you would use a switchback or cluster randomization if there's interference between users (e.g., shared inventory), and highlight the importance of pre-registering the analysis plan to avoid p-hacking.
Formulate a clear, testable hypothesis: adding a 'Work from home' filter will increase bookings per visit by making it easier for remote workers to find suitable listings. Define the primary metric precisely: bookings per visit, aggregated at the user level, and specify secondary metrics like filter usage and booking value.
Choose the randomization unit: typically user-level for marketplace search, but consider cluster randomization if interference exists (e.g., same host or listing). Ensure proper randomization and balance checks, and decide on the treatment/control split (e.g., 50/50).
Apply variance reduction techniques such as CUPED (using pre-experiment data) or stratification to increase power. Consider using a within-user design if possible, and control for covariates like past booking behavior.
Calculate required sample size based on expected effect size, power (80%), significance level (5%), and baseline variance. Use historical data to estimate variance and account for any clustering or intra-class correlation.
Pre-register the analysis plan, monitor for guardrail metrics (e.g., page load time, overall bookings), and set up sequential testing or early stopping rules if needed. Address potential novelty effects and ensure robust rollout decisions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.