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.

HardTechnical

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For a social product, interference (spillover) is likely. Describe experimental designs to measure causal effects in the presence of network spillovers: cluster randomization, graph-cluster randomization, and exposure models. Explain scalability challenges and how you'd identify and report spillover estimates.

MediumTechnical

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What are novelty effects in experiments? Describe how you would detect a novelty effect in product telemetry and two experimental designs to mitigate it. Provide an example metric and how its trajectory would reveal novelty.

MediumTechnical

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A major holiday is approaching and your team wants to run an experiment overlapping the holiday week. Describe approaches to handle seasonality and external events that could bias experiment results. Include pre-experiment adjustments, in-experiment monitoring, and post-hoc analyses you would run.

HardTechnical

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You need to convince the executive team to prioritize a long-term guardrail (e.g., 12-month retention) even though the proposed change improves short-term activation by 8% and reduces short-term churn by 2%. Draft a concise rationale and meeting plan that includes key analyses, risk quantification, rollout plan, and proposed compromise(s).

MediumTechnical

0 practiced

Table: user_metrics(user_id, variant, metric_value). Write an ANSI SQL query to compute: per-variant mean(metric_value), per-variant sample size, difference-in-means (treatment - control), and a 95% confidence interval for the difference assuming independent samples. Describe assumptions you made.

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