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

63 practiced

Write an SQL query to compare two variants across multiple metrics and return a compact table: metric_name, treatment_value, control_value, absolute_lift, relative_lift_percent, p_value. Assume metrics are pre-aggregated per user in table user_metrics(user_id, variant, metric_a, metric_b, metric_c). Describe any assumptions about independence and variance estimation.

MediumTechnical

35 practiced

Explain how you'd detect anomalies in a daily revenue metric using both a statistical method and a machine-learned method. Describe assumptions, pros/cons, and what alerts you'd set to avoid alert fatigue for the growth team.

EasyTechnical

32 practiced

Define leading and lagging indicators in the context of product growth. Give three examples of each for a subscription SaaS product and explain when you would prioritize leading indicators over lagging indicators when measuring an experiment's early impact.

EasyTechnical

40 practiced

Create an actionable template a data scientist can use when delivering inconclusive experiment results to product and leadership. The template should include key sections: summary, statistical evidence, limitations, recommended next steps, and decision options with estimated costs and risks.

HardTechnical

43 practiced

Your growth team wants to optimize for engagement but legal raises concerns about privacy and PII in instrumentation. Propose a metrics strategy that balances business needs with privacy: what metrics to avoid storing, what aggregation/anonymization techniques to apply, and how to validate metric integrity without PII.

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