Design A/B Test to Evaluate New Video-Feed Feature

Quick Overview

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design A/B Test to Evaluate New Video-Feed Feature states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Design A/B Test to Evaluate New Video-Feed Feature

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario A social media company is launching a short-video feed similar to TikTok and wants to evaluate a newly added feature affecting how users engage with the feed. ##### Question Design an A/B test to measure the impact of the new video-feed feature. Cover experiment unit, randomization, success metrics, sample-size needs, guardrails and how you would monitor and conclude the test. Which primary and secondary metrics would you track and why? How would you handle possible novelty and seasonal effects? ##### Hints Think about activation, retention, engagement and negative metrics; outline experiment design and decision criteria.

Quick Answer: This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Design A/B Test to Evaluate New Video-Feed Feature states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Aug 4, 2025, 10:55 AM
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Design A/B Test to Evaluate New Video-Feed Feature

Scenario

A consumer social-media app is launching a short‑video feed (TikTok-style). A newly added feed feature (e.g., UI change, ranking tweak, or interaction control) may alter how users consume and engage with videos.

Task

Design an end-to-end A/B test to measure the feature's impact. Address:

  1. Experiment unit and eligibility criteria.
  2. Randomization and bucketing (including cross-device consistency).
  3. Primary and secondary success metrics with definitions and rationale.
  4. Guardrail/health metrics and thresholds.
  5. Sample-size and test-duration needs; state assumptions and how you would compute MDE/sample size.
  6. Handling novelty effects and seasonality.
  7. Monitoring, analysis plan, and decision criteria.
  8. Risks (e.g., interference/network effects) and mitigations.

Hints: Consider activation, retention, engagement, and negative outcomes. Outline the experiment design and what would make you ship, iterate, or roll back.

Constraints & Assumptions

  • Preserve the scope, facts, inputs, and requested outputs from the prompt above.
  • If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
  • Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

  • Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
  • State assumptions about instrumentation, randomization, sample size, and data quality.
  • Separate descriptive analysis from causal claims.

What a Strong Answer Covers Guidance

  • A metric framework with primary, guardrail, and diagnostic metrics.
  • A credible analysis or experiment design with clear assumptions and bias checks.
  • SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
  • An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions Guidance

  • What sanity checks would you run before trusting the result?
  • How would you handle novelty effects, seasonality, or selection bias?
  • What decision would you make if metrics disagree?
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