Structure your answer around the experiment lifecycle: design (randomization, eligibility, metrics), execution (power, duration, ramp, instrumentation), and analysis (pre-registration, variance reduction, conflict resolution). Emphasize how you mitigate unique challenges like network effects in group chats and heavy-tailed engagement. Conclude with a decision framework for interpreting conflicting signals.
Pro tip: Propose using a cluster-randomized design at the group-chat level to address SUTVA violations, and pre-register a switchback or holdout to validate long-term effects. Show you understand Meta's scale by discussing how to handle multi-account users via a unified user graph and how to detect novelty via pre-period behavior.
Specify randomization unit (user or group chat), eligibility criteria (e.g., active users with unread messages), and pre-exposure filters to exclude users already exposed to the feature. Address cross-contamination by using cluster randomization for group chats and isolating treatment at the user level for 1:1 chats.
Choose primary metric (e.g., unread message click-through rate), secondary metrics (e.g., session duration, messages sent), and guardrails (e.g., user retention, notification opt-outs). Ensure metrics are sensitive to the feature and aligned with company goals.
Account for heavy-tailed engagement by using variance reduction techniques (CUPED, stratification) and robust power analysis (e.g., bootstrap or quantile-based). Determine test duration to cover seasonality and novelty effects, with a ramp plan (e.g., 1% -> 5% -> 50%) to monitor early signals.
For group chats, use cluster randomization (by group) to prevent spillover. For multi-account users, unify accounts to avoid contamination. Consider switchback designs or holdouts to measure long-term effects and validate SUTVA assumptions.
Pre-register the analysis plan including variance reduction, multiple testing corrections, and decision rules for conflicting metrics (e.g., primary wins but guardrail degrades). Use sequential testing or Bayesian methods to allow early stopping if needed.
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