Experimentation Metrics and Strategy Questions

Designing experiments and selecting appropriate primary, secondary, and guardrail metrics to evaluate hypotheses while protecting long term user value. This includes choosing metrics that reflect both short term signal and long term outcomes, reasoning about metric interactions and potential unintended consequences, and applying statistical considerations such as minimum detectable effect, sample size and power analysis, test duration, and external validity across segments and platforms. Candidates should also discuss experiment risk mitigation, stopping rules, and how to operationalize experiment results into product decisions.

EasyTechnical

91 practiced

List the main differences you'd expect when running A/B tests on mobile apps versus web (browser) experiments. As an engineer, what platform-specific risks and instrumentation differences would you emphasize in a pre-launch checklist?

EasyTechnical

53 practiced

Describe at least three practical guardrail metrics you would set up for a mobile app's personalization experiment that changes content ranking. For each guardrail, explain why it's important and how you'd instrument and alert on it.

EasyTechnical

60 practiced

Why are holdout or control groups important when measuring long-term business impact of product changes? As an engineer, describe how you'd maintain a long-running holdout for six months while making many product changes in the system.

MediumTechnical

105 practiced

Describe how you would compute a cohort-based retention metric using survival analysis concepts when some users have not yet churned (right-censoring). What statistical method would you use, how would you implement it at scale, and how would you visualize results for stakeholders?

MediumTechnical

74 practiced

Write a Python script to detect Sample Ratio Mismatch (SRM) for a simple two-variant experiment using a chi-squared test. Input: counts dictionary {'control': n1, 'treatment': n2}. Output: observed proportion, expected proportion, chi2 statistic, and p-value. Include input validation and an example run.

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