← Microsoft Interview Insights
Structure your answer around the full experimentation lifecycle: hypothesis, design, execution, analysis, and decision-making. Emphasize statistical rigor, practical constraints, and how you would handle common pitfalls like low sample size or novelty effects. Tailor to ML by discussing model-based metrics and online evaluation.
Pro tip: Always mention guardrail metrics and the importance of pre-registering your analysis plan to avoid p-hacking. Also, discuss how you would handle network effects or interference if the experiment involves social or marketplace features.
Start with a clear, testable hypothesis and define primary, secondary, and guardrail metrics. Ensure metrics are aligned with business goals and sensitive to the change.
Determine randomization unit (user, session, etc.), sample size via power analysis, and duration. Consider A/A tests to validate the setup and address potential confounders.
Launch the test, monitor for data quality issues, and ensure no sample ratio mismatch (SRM). Track guardrail metrics to catch unintended negative impacts.
Use appropriate statistical tests (e.g., t-test, bootstrap) to compare groups. Check for novelty effects, segment-level differences, and practical significance.
Decide whether to ship, iterate, or abandon based on results. Document learnings and consider follow-up experiments to refine.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.