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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.

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.

Design A/B Test for Short-Video Recommendation Algorithm

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competency in experimental design, statistical power analysis, precise metric definition, and evaluation of recommendation algorithms for short‑video feeds, and is categorized under Analytics & Experimentation.

A/B Test: New Short‑Video Recommendation Algorithm

Context

You are evaluating a new recommendation algorithm for a TikTok‑like short‑video feed. The goal is to measure whether the new algorithm increases user engagement without harming experience quality.

Tasks

List the top three user‑engagement metrics you would track and define them precisely.
Design the A/B test, including:
- Experiment unit and randomization.
- Sample‑size calculation (show the power analysis).
- Experiment duration (connect to your minimum detectable effect).
- Stopping criteria (statistical and practical).

Solution

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Design A/B Test for Short-Video Recommendation Algorithm | Meta Interview Question