How do I approach Analytics & Experimentation interview questions?

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

What difficulty level is this interview question?

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.

Explain Algorithm's Disproportionate Impact on Demographic Segments | Meta Interview Question

Explain Algorithm's Disproportionate Impact on Demographic Segments

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's ability to analyze experimental results, reason about causal inference and subgroup heterogeneity, and interpret CTR lifts in ad-ranking contexts, and is categorized under Analytics & Experimentation.

Ad-Ranking A/B Test: Interpreting Heterogeneous CTR Lifts

Context

You ran a standard A/B experiment for a new ad-ranking algorithm. The primary metric is CTR (clicks ÷ impressions). The experiment shows:

Overall lift: +5% relative CTR
Specific segment (Indian males, age 18–24): +100% relative CTR

Assume randomization at the user level, with typical ad auction dynamics and repeated exposures per user.

Questions

Hypotheses: What could explain an overall +5% lift while a specific demographic shows +100%?
Statistical validity: How would you validate that the segment lift is statistically significant and not due to random noise or confounding?
Pre-rollout diligence: What additional metrics, slices, and checks would you examine before a global rollout?

Solution

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