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.

MediumTechnical

0 practiced

An experiment produced a statistically significant improvement in a micro-conversion metric (CTR +0.5%, p<0.01) but the expected revenue impact is small. Explain how you would evaluate practical/business significance, compute expected revenue impact (show formula), and recommend whether to roll out, run further tests, or reject the change.

EasyTechnical

0 practiced

What is a guardrail metric? Give one concrete example of a guardrail you would set when launching in-app subscriptions, specify how you'd define it, and explain how you'd choose its alert threshold and monitoring cadence.

HardTechnical

0 practiced

Conversion rate dropped 8% after a release. Given the sample schemas below, propose a detailed root-cause analysis plan: list hypotheses, exact SQL queries you would run (sketch them), cohort analyses, logs/metrics to check, instrumentation or ETL issues to validate, and a set of quick experiments to validate leading hypotheses.

events(user_id uuid, event_name text, occurred_at timestamp, platform text, country text)
purchases(user_id uuid, amount numeric, purchased_at timestamp)

Be specific about which cohorts and time windows you'd compare.

HardSystem Design

0 practiced

Create an end-to-end plan to test whether a change in onboarding copy increased long-term retention. Describe the metric definitions (primary and guardrails), instrumentation required, experiment design (randomization, duration, cohorts), statistical tests to use, how to detect novelty effects, and criteria for calling the test successful.

HardTechnical

0 practiced

Build a prioritized investigative checklist a PM should use when a key metric regresses overnight during a major holiday. Include quick triage, data validation steps, stakeholders to loop in, short-term mitigations (e.g., rollback), and a communications plan to internal teams and external customers if necessary.

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