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.

MediumSystem Design

68 practiced

You need to compare three price points for a subscription and also test a UI redesign simultaneously. Should you run a factorial multivariate experiment, a two-stage sequential test, or separate A/B tests? Design the experiment choice, allocation plan, primary hypotheses, and how you will correct for multiple comparisons.

HardTechnical

60 practiced

Design detection and correction methods for biased treatment assignment caused by bot traffic or subtle instrumentation failures that do not trigger alarms. Include algorithmic detection techniques, ways to adjust causal estimates (e.g., robust reweighting), and post-rollout remediation and validation steps.

HardTechnical

65 practiced

Prepare a quantitative cost-benefit framework for leadership weighing rapid decentralized feature testing against centralized experiment review that slows velocity but reduces false positives. Include expected value of information, expected rollout cost of false positives, team throughput metrics, and recommend a hybrid governance policy with thresholds for centralized review.

EasyTechnical

61 practiced

Define the Minimum Detectable Effect (MDE) and explain how it relates to statistical power, significance level (alpha), and required sample size for online A/B tests. Use a brief numeric example: baseline conversion 5%, power 80%, alpha 0.05, and discuss qualitatively how required sample size changes if MDE is 0.5% versus 2%. Mention assumptions behind normal approximation for binary outcomes.

EasyTechnical

70 practiced

Explain sequential testing and interim analysis. Describe why optional stopping inflates Type I error in naive analyses and name one frequentist and one Bayesian method to allow looks at the data while maintaining valid inference.

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