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.

MediumSystem Design

0 practiced

Design a high-level experiment-metrics pipeline to produce daily aggregated metrics for experiments at a scale of 10M events/day. Include event ingestion, deduplication, user-level aggregation, bucketing, and daily reporting. State assumptions and critical SLAs (latency, correctness).

MediumTechnical

0 practiced

Compare a Bayesian stopping rule that uses posterior probability of lift > 0 to a frequentist fixed-horizon test with p < 0.05. As an engineer, discuss implementation complexity, interpretability for stakeholders, and how each approach affects time-to-decision.

MediumTechnical

0 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.

HardTechnical

0 practiced

Design a detection and mitigation pipeline for bot or fraud traffic that contaminates experiment metrics. Include signal sources (rate spikes, impossible sequences), automated blocks, and a human-review flow. How would you quantify the impact of bots on an experiment's primary metric?

HardTechnical

0 practiced

Explain the Sequential Probability Ratio Test (SPRT) and outline how you would implement a sequential test for a continuous metric in an experimentation platform. Discuss alpha control, expected sample path behavior, and how you would simulate operating characteristics before productionizing.

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