Statistical Foundations for Experimentation Questions

Core statistical concepts and inference needed to design analyze and interpret experiments. Topics include hypothesis testing p values confidence intervals Type One and Type Two errors the relationship between sample size variability and interval width statistical power minimum detectable effect and effect size versus practical significance. Candidates should be able to choose and explain common statistical tests such as t tests and chi square tests contrast Bayesian and frequentist approaches at a conceptual level and describe variance estimation and variance reduction techniques. The topic covers corrections for multiple comparisons sequential testing and the risks of peeking and p hacking common misconceptions about p values and limitations of inference such as confounding and selection bias. Candidates should also be able to translate statistical findings into clear language for non technical stakeholders and explain uncertainty and limitations.

HardTechnical

0 practiced

After deploying a model change that was supported by a positive experiment, you observe post-deployment metric drift: the initial lift disappears and some metrics degrade. Walk through steps to diagnose whether this is caused by confounding, selection bias, implementation differences, non-stationarity, or metric-definition mismatches. Include specific data checks, logging, causal DAG reasoning, and when to run follow-up experiments.

MediumTechnical

0 practiced

Describe how covariate adjustment (e.g., ANCOVA or regression adjustment) can increase power in randomized experiments. Write the standard regression equation (outcome ~ treatment + covariates), explain how to interpret the treatment coefficient, and list best practices such as pre-specifying covariates and avoiding post-treatment variables.

HardSystem Design

0 practiced

Design a system and statistical workflow to run continuous adaptive A/B/n experiments for feature rollouts while controlling false discoveries across many hypotheses and over time. Consider FDR control, hierarchical testing, pre-registration, experiment reuse, and monitoring. Describe architectural components, offline analysis pipelines, and safeguards (technical and process) to limit long-term inflation of false positives.

HardTechnical

0 practiced

Tell me about a time you had to convince engineering or product stakeholders to delay a model or feature rollout because the experiment was underpowered or results were ambiguous. If you do not have a direct example, describe the hypothetical conversation: what evidence you would present (power calculation, confidence intervals, potential false positive/negative risks), how you'd quantify the business risk, and what concrete next steps you'd propose (increase sample, pilot, redefine metric).

MediumTechnical

0 practiced

Explain when cluster-randomized trials are appropriate (for example, when treatment is applied at group or network level), how the design effect inflates the required sample size, and how to analyze clustered data using cluster-robust standard errors. Provide the design effect formula: DE = 1 + (m - 1) * ICC and explain each term.

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