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Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

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This is a medium difficulty Analytics & Experimentation question, commonly asked during Onsite rounds at Meta.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Meta during technical interviews.

Reduce variance with covariate adjustment | Meta Interview Question

Reduce variance with covariate adjustment

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in experimental design, variance reduction techniques, and regression-based covariate adjustment (CUPED/ANCOVA) within randomized A/B tests, including understanding of statistical power and diagnostic checks.

Experiment Design and CUPED/Regression Adjustment

You are running a randomized A/B test with outcome Y. You also have a pre-period covariate X (measured before any treatment exposure) that explains 36% of the variance in Y (i.e., R² = 0.36) when regressing Y on X.

Answer the following:

(a) Derive how CUPED (a.k.a. regression/ANCOVA adjustment using a pre-period covariate) changes the variance of the treatment effect estimator Δ, and quantify the expected sample-size savings and MDE change given R² = 0.36.

(b) List assumptions and failure modes that would invalidate or undermine CUPED (e.g., post‑treatment leakage, mis‑timed covariates), and diagnostics you would run to detect problems.

Solution

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