This question is basically six questions stapled together and they want all of it.
Structure your answer around the experiment lifecycle: define the hypothesis and metrics, design the test (exposure, randomization, power), plan the analysis, and outline operational risks and decision rules. Emphasize trade-offs and practical considerations specific to video ads at Meta's scale.
Pro tip: Proactively discuss how you would handle network effects and interference, which are critical in social media experiments, and propose solutions like cluster randomization or switchback tests.
State a clear hypothesis (e.g., new video ad format increases click-through rate) and select primary and guardrail metrics. Ensure metrics align with business goals and are sensitive to the change.
Specify exposure (e.g., user sees ad in feed), randomization unit (user-level), and power calculations (sample size, duration). Consider interference and choose randomization unit accordingly.
Outline statistical tests (e.g., t-test, CUPED), handling of multiple comparisons, and subgroup analyses. Pre-register the analysis to avoid p-hacking.
Identify risks like novelty effects, SRM, and technical issues. Plan for monitoring, early stopping rules, and data quality checks.
Define criteria for success (e.g., statistically significant lift in primary metric without guardrail degradation) and actions based on results (ship, iterate, or abandon).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by validating the experiment's integrity and data quality to rule out implementation or measurement issues. If the experiment is valid, dig into segment-level and secondary metrics to uncover heterogeneous effects or leading indicators. Finally, decide whether to iterate, pivot, or kill the feature based on the insights and business context.
Pro tip: Always check guardrail metrics and novelty effects before concluding the feature failed—sometimes the primary metric is flat but user experience improved in other ways. Also, consider that a flat primary metric with positive secondary metrics might justify further investment.
Check for sample ratio mismatch (SRM), data pipeline issues, and metric definitions to ensure the experiment ran correctly. Confirm that the null result is not due to a bug or insufficient power.
Break down results by user segments (e.g., new vs. existing, platform, geography) to detect heterogeneous treatment effects. Examine secondary and guardrail metrics for any meaningful changes.
If segments show effects, hypothesize why the primary metric didn't move overall (e.g., dilution, cannibalization). Use qualitative data (user feedback, session replays) to understand user behavior.
Based on findings, recommend either iterating on the feature (e.g., targeting specific segments), running a follow-up experiment, or discontinuing. Align with stakeholders on the decision criteria.
Summarize insights and communicate them to the team to inform future experiments and product strategy. Emphasize that a null result is still valuable knowledge.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.