Compute robust inference under skew and outliers
Company: Voleon
Role: Data Scientist
Category: Statistics & Math
Difficulty: hard
Interview Round: Technical Screen
Overview: This question evaluates a data scientist's competency in robust statistical inference for A/B testing, covering handling of skewed continuous outcomes, extreme outliers, heteroskedasticity, missingness, multiplicity, selection between test statistics, construction of confidence intervals (including bootstrap and transformed CIs), power approximation, and robust estimators like trimmed means or M‑estimators. It is commonly asked in the statistics and experimental-design domain because it probes both conceptual understanding of robustness and multiple-testing principles and practical application skills in selecting appropriate inference methods, computing intervals and power under realistic data issues, and interpreting variance-stabilizing transformations.