Defining and Using Success Metrics Questions
Learn to propose metrics that directly tie to business or product goals. Understand primary metrics (direct measure of success, like feature adoption rate or API call volume) versus secondary metrics (supporting indicators like latency, error rates, or user satisfaction). Practice proposing 2-3 realistic metrics for different scenarios. At entry-level, you don't need statistical sophistication, but you should understand how to measure whether something worked and why certain metrics matter.
HardTechnical
0 practiced
You maintain an anomaly detection system for business metrics. Describe how you would set detection thresholds to balance false positives and false negatives given different cost structures (e.g., false negative cost is 10x false positive). Show how you would incorporate cost into threshold selection and evaluation.
HardTechnical
0 practiced
You run many experiments that each report dozens of metrics. Explain practical approaches to control for multiple comparisons to avoid false discoveries and maintain trust in results. Cover pre-registration, hierarchical testing, FDR control, and business-friendly alternatives you might use.
MediumTechnical
0 practiced
You're tasked with instrumenting a funnel to measure drop-off across 4 steps (signup, verification, profile-complete, first-purchase). List required events/attributes to capture for accurate measurement, explain how you'd implement exposure tracking, and describe one SQL query pattern you would use to calculate funnel conversion rates by cohort.
MediumTechnical
0 practiced
Discuss the trade-offs between using DAU (Daily Active Users) and MAU (Monthly Active Users) as the primary engagement metric for a social app. For each trade-off, propose a scenario where DAU is preferred and another where MAU is preferred.
EasyTechnical
0 practiced
Given the events table schema: events(user_id bigint, event_name varchar, occurred_at timestamp, platform varchar), write a SQL query (Postgres) to compute Weekly Active Users (WAU) for the past 4 calendar weeks broken down by platform. Define WAU as number of unique users with any event in that week. Explain any assumptions about time zones and late-arriving events.
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