PracHub
QuestionsPremiumLearningGuidesInterview PrepCoaches
|Home/Analytics & Experimentation/Roblox

Design experiment for homepage tab replacement

Last updated: Mar 29, 2026

Quick Overview

This question evaluates experiment design and causal-inference competency for product changes that impact both end users and platform creators, covering randomization strategies, metric and guardrail selection, power calculations, and handling of novelty, navigation friction, and ecosystem interference.

  • hard
  • Roblox
  • Analytics & Experimentation
  • Data Scientist

Design experiment for homepage tab replacement

Company: Roblox

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: hard

Interview Round: Technical Screen

Roblox plans to replace an existing homepage tab with a new tab. Design a rigorous experiment to evaluate the causal impact of this replacement at the user and ecosystem levels. Assume today is 2025-09-01. Specify: (1) the experimental unit (e.g., user-level vs. device-level) and randomization scheme that avoids cross-device contamination; (2) primary success metrics (e.g., D1/D7 retention, homepage click-through, session length, conversion to play, creator impressions/engagement, revenue) and guardrails (crash rate, latency, abuse, fairness of content distribution); (3) how you will mitigate and measure novelty and navigation-friction effects when a tab is removed (e.g., ramp plan, cooldowns, or holdout re-exposure); (4) how to handle ecosystem interference/network effects (e.g., switchback or geo experiments, creator-level holdouts); (5) the metric definitions at a user-day grain and whether to use CUPED or pre-exposure covariates; (6) sample size/power calculations for a 1% relative lift in D7 retention with 80% power and α=0.05, including assumptions and minimum test duration under weekly seasonality; (7) decision thresholds and rollback criteria; (8) what diagnostics you would run if click-through rises but D7 retention falls; (9) a follow-up analysis plan if the treatment cannibalizes time from other tabs but increases long-term retention (e.g., 2x2 or crossover design, or diff-in-diff with staggered rollout).

Quick Answer: This question evaluates experiment design and causal-inference competency for product changes that impact both end users and platform creators, covering randomization strategies, metric and guardrail selection, power calculations, and handling of novelty, navigation friction, and ecosystem interference.

Related Interview Questions

  • How to estimate feature impact on usage time - Roblox (easy)
  • How to estimate a feature’s causal impact on time spent - Roblox (medium)
  • Compute DID estimate and pretrend flag - Roblox (hard)
  • Compute minimum sample size for A/B test - Roblox (hard)
  • Compute DiD and validate parallel trends - Roblox (hard)
Roblox logo
Roblox
Oct 13, 2025, 9:49 PM
Data Scientist
Technical Screen
Analytics & Experimentation
3
0

Experiment Design: Replacing a Homepage Tab and Measuring User + Ecosystem Impact

Assume today is 2025-09-01. Roblox plans to replace an existing homepage tab with a new tab. Design a rigorous experiment to estimate the causal impact at both the user level and the creator/ecosystem level.

Specify the following:

  1. Experimental unit and randomization
    • Choose the experimental unit (e.g., user-level vs. device-level) and define a randomization scheme that avoids cross-device contamination.
  2. Metrics and guardrails
    • Primary success metrics (e.g., D1/D7 retention, homepage click-through, session length, conversion to play, creator impressions/engagement, revenue).
    • Guardrails (e.g., crash rate, latency, abuse, fairness of content distribution).
  3. Novelty and navigation-friction mitigation
    • How you will mitigate and measure novelty and navigation-friction effects when a tab is removed (e.g., ramp plan, cooldowns, holdout re-exposure).
  4. Ecosystem interference/network effects
    • How to handle ecosystem interference (e.g., switchback or geo experiments, creator-level holdouts) and measure supply-side externalities.
  5. Metric definitions and covariates
    • Define metrics at a user-day grain. State whether you will use CUPED or pre-exposure covariates and how.
  6. Power and duration
    • Sample size/power calculations for detecting a 1% relative lift in D7 retention with 80% power and α = 0.05. Include assumptions and minimum test duration accounting for weekly seasonality.
  7. Decisions and rollback
    • Decision thresholds for ship/no-ship and rollback criteria.
  8. Diagnostics for counterintuitive results
    • What diagnostics you would run if homepage click-through rises but D7 retention falls.
  9. Follow-up design for cannibalization vs. long-term retention
    • A follow-up analysis plan if the treatment cannibalizes time from other tabs but increases long-term retention (e.g., 2×2 or crossover, or diff-in-diff with staggered rollout).

Solution

Show

Comments (0)

Sign in to leave a comment

Loading comments...

Browse More Questions

More Analytics & Experimentation•More Roblox•More Data Scientist•Roblox Data Scientist•Roblox Analytics & Experimentation•Data Scientist Analytics & Experimentation
PracHub

Master your tech interviews with 7,500+ real questions from top companies.

Product

  • Questions
  • Learning Tracks
  • Interview Guides
  • Resources
  • Premium
  • For Universities
  • Student Access

Browse

  • By Company
  • By Role
  • By Category
  • Topic Hubs
  • SQL Questions
  • Compare Platforms
  • Discord Community

Support

  • support@prachub.com
  • (916) 541-4762

Legal

  • Privacy Policy
  • Terms of Service
  • About Us

© 2026 PracHub. All rights reserved.