Structure your answer around the experiment lifecycle: define the population and randomization, specify metrics and analysis approach, calculate sample size, address biases and fraud, and conclude with a decision framework. Emphasize trade-offs and practical considerations, showing you can balance statistical rigor with business constraints.
Pro tip: Highlight the importance of pre-registering the analysis plan and guardrail metrics to prevent p-hacking and ensure trustworthy results. Also, discuss how to handle network effects or interference in a subscription-based product, as this is often overlooked.
Specify who is eligible (e.g., new users, non-subscribers) and how to randomize them (e.g., user-level randomization with stratification). Ensure randomization is consistent and documented.
Select the primary metric (e.g., paid conversion rate within X days) and define the observation window that captures the trial period plus a buffer. Also, list secondary and guardrail metrics.
Choose between intent-to-treat (ITT) and triggered analysis, justifying your choice. Calculate sample size based on baseline conversion, minimum detectable effect, power, and significance level, accounting for the observation window.
Identify potential biases (e.g., selection bias, novelty effect) and mitigation strategies. Discuss fraud detection (e.g., multiple accounts) and interference (e.g., network effects) and how to handle them.
Define criteria for launch: statistical significance, practical significance, guardrail metrics not degraded, and qualitative insights. Include a plan for segment analysis and follow-up experiments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.