Experimentation Strategy and Advanced Designs Questions

When and how to use advanced experimental methods and how to prioritize experiments to maximize learning and business impact. Candidates should understand factorial and multivariate designs interaction effects blocking and stratification sequential testing and adaptive designs and the trade offs between running many factors at once versus sequential A and B tests in terms of speed power and interpretability. The topic includes Bayesian and frequentist analysis choices techniques for detecting heterogeneous treatment effects and methods to control for multiple comparisons. At the strategy level candidates should be able to estimate expected impact effort confidence and reach for proposed experiments apply prioritization frameworks to select experiments and reason about parallelization limits resource constraints tooling and monitoring. Candidates should also be able to communicate complex experimental results recommend staged follow ups and design experiments to answer higher order questions about interactions and heterogeneity.

EasyTechnical

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Explain p-values and confidence intervals in the context of A/B testing. What does a 95% confidence interval on the treatment effect mean practically? Describe two common misunderstandings when communicating p-values and CIs to non-technical stakeholders and how you'd avoid them.

MediumTechnical

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You plan a cluster-randomized trial where entire cities are randomized to a marketing treatment. Explain how intraclass correlation (ICC) affects power, show the design effect formula, and describe strategies to mitigate high ICC when you have limited clusters.

EasyTechnical

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Describe how you would choose a primary metric and three guardrail metrics for a growth experiment that introduces a new onboarding flow aimed at increasing conversion to paid subscription. Justify why each metric is primary or a guardrail.

EasyTechnical

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Explain the key components of a standard A/B test for a web product: hypothesis, randomization mechanism, treatment and control groups, the primary metric and guardrail metrics, sampling plan, and evaluation window. Describe why randomization matters for causal claims and list the main assumptions required (e.g., SUTVA, no post-randomization confounding).

MediumTechnical

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Compare Bayesian decision thresholds and frequentist p-value thresholds for stopping experiments when mistakes have asymmetric costs (e.g., false negative cost >> false positive). How would you choose priors and decision rules in a Bayesian framework to reflect asymmetric loss?

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