Metrics Selection and Diagnostic Interpretation Questions

Addresses how to choose appropriate metrics and how to interpret and diagnose metric changes. Includes selecting primary and secondary metrics for experiments and initiatives, balancing leading indicators against lagging indicators, avoiding metric gaming, and handling conflicting signals when different metrics move in different directions. Also covers anomaly detection and root cause diagnosis: given a metric change, enumerate potential causes, propose investigative steps, identify supporting diagnostic metrics or logs, design quick experiments or data queries to validate hypotheses, and recommend remedial actions. Communication of nuanced or inconclusive results to non technical stakeholders is also emphasized.

HardTechnical

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Create a checklist of 12 diagnostic queries or checks you would run automatically when any primary KPI drops more than 5% day-over-day. Include SQL pseudo-queries or descriptions, expected suspicious outputs, and who to notify for each check.

MediumTechnical

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Design an anomaly detection approach for daily conversion rate where per-day sample size is small (low traffic). Discuss choices for aggregation, statistical tests, bootstrapping, or Bayesian methods to detect meaningful changes while limiting false positives.

HardSystem Design

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You want to detect and prevent accidental cross-contamination between concurrent experiments (users exposed to multiple experiments affecting the same metric). Propose instrumentation and assignment strategies, analytical methods to estimate contamination, and practical controls engineering and product teams can implement.

EasyTechnical

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You notice yesterday's conversion rate dropped 7% for the checkout page. List the top 8 immediate diagnostic checks you would run (data, instrumentation, external factors, product changes). For each check, specify what a suspicious result looks like and a quick query or log you would run to confirm it.

MediumTechnical

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A product team wants a single-signup KPI that is easy to present, but you worry it can be gamed. Propose a small set (3–4) of complementary KPIs that together give a robust view of signup health, explain how they triangulate to prevent gaming, and suggest policies to enforce integrity.

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