Metrics, Guardrails, and Evaluation Criteria Questions
Design appropriate success metrics for experiments. Understand primary metrics, secondary metrics, and guardrail metrics. Know how to choose metrics that align with business goals while avoiding unintended consequences.
HardTechnical
0 practiced
Propose a statistical and machine learning approach to estimate heterogeneous treatment effects (HTE) across user segments for an A/B test. Discuss methods like causal forests or uplift models, how you'd validate results, control for overfitting and multiple comparisons, and make the outputs interpretable for product managers.
MediumTechnical
0 practiced
Baseline conversion is 2%. You want 80% power to detect a 10% relative lift (i.e., from 2% to 2.2%) with alpha=0.05 (two-tailed). Show the sample size calculation per group and state assumptions. Provide the formula and numeric result.
MediumTechnical
0 practiced
Describe a pragmatic framework for selecting guardrail metrics for a consumer app that is optimizing for growth but risks harming retention or content quality. Include how you would set thresholds and prioritize guardrails.
EasyTechnical
0 practiced
List five common pitfalls when selecting primary metrics for experiments and for each give a short mitigation strategy. Examples might include metric stability, low power, gameability, misalignment with business value, and delayed signals.
EasyBehavioral
0 practiced
Tell me about a time you proposed a new metric to stakeholders but faced resistance. Describe the situation, how you framed the metric's value, the steps you took to get buy-in, and the eventual outcome.
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