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Test if social users are more engaged

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competencies in observational analytics, engagement metric selection, cohort construction for overlapping behaviors, statistical comparison and bias/confounding assessment, and sits in the Analytics & Experimentation domain of Data Science.

  • Hard
  • Meta
  • Analytics & Experimentation
  • Data Scientist

Test if social users are more engaged

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: Hard

Interview Round: Onsite

A PM has a hypothesis: **users who use “social” apps are more engaged on a regular basis than users who use “game” apps.** You have the same tables: - `user_activity(user_id, date, app_id, session_id, duration)` - `apps(app_id, app_name, app_category)` ## Task Describe how you would **evaluate** this hypothesis using historical data. Your answer should cover: 1) How you would define **engagement** (choose a primary metric and 1–2 diagnostic metrics). 2) How you would define comparison groups when many users use **both** categories (e.g., “social-only”, “game-only”, and mixed users; or thresholding by % time). 3) What statistical test/model you would use to compare groups, and what assumptions you’d check. 4) What you would do if the “social-only” and “game-only” groups are small (e.g., n≈300 each) but the mixed group is very large (e.g., n≈100,000). 5) Key confounders/biases you would worry about and how you’d mitigate them (e.g., user tenure, overall activity level, seasonality).

Quick Answer: This question evaluates a data scientist's competencies in observational analytics, engagement metric selection, cohort construction for overlapping behaviors, statistical comparison and bias/confounding assessment, and sits in the Analytics & Experimentation domain of Data Science.

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Meta
Aug 17, 2025, 12:00 AM
Data Scientist
Onsite
Analytics & Experimentation
1
0
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A PM has a hypothesis: users who use “social” apps are more engaged on a regular basis than users who use “game” apps.

You have the same tables:

  • user_activity(user_id, date, app_id, session_id, duration)
  • apps(app_id, app_name, app_category)

Task

Describe how you would evaluate this hypothesis using historical data.

Your answer should cover:

  1. How you would define engagement (choose a primary metric and 1–2 diagnostic metrics).
  2. How you would define comparison groups when many users use both categories (e.g., “social-only”, “game-only”, and mixed users; or thresholding by % time).
  3. What statistical test/model you would use to compare groups, and what assumptions you’d check.
  4. What you would do if the “social-only” and “game-only” groups are small (e.g., n≈300 each) but the mixed group is very large (e.g., n≈100,000).
  5. Key confounders/biases you would worry about and how you’d mitigate them (e.g., user tenure, overall activity level, seasonality).

Solution

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