Experimentation Methodology and Rigor Questions

Focuses on rigorous experimental methodology and advanced testing approaches needed to produce reliable, actionable results. Topics include statistical power and minimum detectable effect trade offs, multiple hypothesis correction, sequential and interim analysis, variance reduction techniques, heterogenous treatment effects, interference and network effects, bias in online experiments, two stage or multi component testing, multivariate designs, experiment velocity versus validity trade offs, and methods to measure business impact beyond proximal metrics. Senior level discussion includes designing frameworks and practices to ensure methodological rigor across teams and examples of how to balance rapid iteration with safeguards to avoid false positives.

HardTechnical

60 practiced

What is an alpha-spending function and how does it differ from fixed group-sequential boundaries (e.g., Pocock, O'Brien–Fleming)? Provide a short example of choosing an alpha-spending approach suitable for business experiments that require frequent interim checks early in the experiment lifecycle.

HardTechnical

77 practiced

Case study: A subscription product measures short-term trial signups (proximal) and lifetime value (LTV) (long-term). The marketing team wants to run many experiments to optimize trial signups because they move quickly. As an applied scientist, draft an experimental policy that balances velocity with the risk of optimizing a metric that does not improve LTV. Include guidelines for metric hierarchy, minimum holdout percentage, experiment duration, and escalation criteria for deployment.

EasyTechnical

67 practiced

Define statistical power, Type I and Type II errors, significance level (alpha), and minimum detectable effect (MDE) in the context of online A/B testing. Then, given a baseline conversion rate of 5% and a desired two-sided alpha=0.05 and power=80%, explain qualitatively how the required sample size changes as you (a) halve the MDE, (b) change to a one-sided test, and (c) increase desired power to 90%. No calculations required — focus on intuition and trade-offs.

HardTechnical

82 practiced

Design an experiment to estimate heterogeneous treatment effects (HTE) for a homepage redesign. Describe how you would (a) pre-specify segments vs use data-driven methods, (b) estimate HTE (e.g., causal forests, uplift models), and (c) validate discovered heterogeneity to avoid spurious claims.

HardTechnical

81 practiced

You need to correct for multiple correlated metrics across 30 tests run weekly. Propose a practical, scalable correction strategy to report significant findings to product teams (consider runtime, interpretability, and conservativeness). Justify your choice.

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