← Google DeepMind Interview Insights
I started rattling off metrics to check and realized halfway through I hadn't said anything about what the goal of the experiment was in the first place.
Start by clarifying the experiment's goal and success metrics, then evaluate statistical significance and practical significance. Consider broader strategic factors like long-term impact, user experience, and alignment with company mission before making a ship/no-ship decision.
Pro tip: Always check for novelty effects and segment-level impacts—sometimes a feature that looks flat overall can be a big win for a key user segment or have hidden long-term benefits.
Revisit the hypothesis and pre-defined primary and guardrail metrics. Ensure you know what threshold constitutes success (e.g., minimum detectable effect).
Check if the experiment ran long enough, has sufficient power, and if results are statistically significant. Look for anomalies like sample ratio mismatch or novelty effects.
Even if statistically significant, consider if the effect size is meaningful for the business. Calculate potential ROI and impact on key goals.
Weigh long-term effects, user trust, brand alignment, and qualitative feedback. Think about whether the feature aligns with the product vision and company mission.
Decide to ship, iterate, or kill the feature. If shipping, plan for monitoring and further optimization; if not, document learnings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.