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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 Experiment to Test New Hashtag Recommender Algorithm

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's experiment design competency, including A/B testing fundamentals, randomization unit choice, stratification/blocking, sample size and power considerations, guardrail metrics, and exposure/interference handling for online recommender systems.

Experiment Design: Testing a New Hashtag Recommendation Algorithm

Context

A social app shows hashtag recommendations to users while composing posts. A new algorithm is proposed to increase hashtag adoption and downstream engagement without harming reliability or content quality.

Task

Design and run an online experiment to evaluate the new recommender. Focus on how you would choose test and control groups, and address:

Randomization unit
Stratification/blocking
Sample size and statistical power
Guardrail metrics
Exposure overlap/interference
Practical run plan (ramp, duration, analysis)

Solution

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