##### Scenario Facebook Groups team wants to understand current product health and evaluate a proposed 'comment collapsing' feature. ##### Question What metrics would you track to judge the performance of Facebook Groups today? How would you design an experiment to decide whether the new comment-collapsing feature should be launched? ##### Hints Define north-star and guardrail metrics, outline experiment design, success criteria, and potential trade-offs.

This question evaluates a data scientist's skills in product analytics, experiment design, causal inference, metric selection (north-star and guardrails), and trade-off assessment for a social-platform group discussion feature.

How do I approach Analytics & Experimentation interview questions?

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.

Evaluate Facebook Groups Metrics and Test Comment-Collapsing Feature

Facebook Groups Product Health and Feature Experiment Design

Context

You are evaluating the current health of Facebook Groups and deciding whether to launch a proposed "comment collapsing" feature. Assume the feature automatically collapses some comments within Group posts (e.g., low-ranked, off-topic, or long subthreads), with an affordance to expand. The goal is to reduce clutter and help people find valuable comments more efficiently without harming group engagement or safety.

Tasks

Define the core (north-star) and guardrail metrics you would use to judge the overall health of Facebook Groups today.
Design an experiment to evaluate whether the comment-collapsing feature should be launched. Include:
- Hypotheses and success criteria
- Experiment unit and randomization
- Key metrics (primary, secondary, guardrails)
- Duration, sample size/power approach
- Analysis plan and heterogeneity cuts
- Risks, trade-offs, and mitigations

Hints

Be explicit about what the north-star captures and what guardrails protect.
Outline how you would measure quality, satisfaction, and safety.
Consider network effects, interference, and bias in your design.
Propose practical thresholds for launch vs. iterate vs. do-not-launch.

Context

Tasks

Define the core (north-star) and guardrail metrics you would use to judge the overall health of Facebook Groups today.

Design an experiment to evaluate whether the comment-collapsing feature should be launched. Include:

Hypotheses and success criteria
Experiment unit and randomization
Key metrics (primary, secondary, guardrails)
Duration, sample size/power approach
Analysis plan and heterogeneity cuts
Risks, trade-offs, and mitigations

Evaluate Facebook Groups Metrics and Test Comment-Collapsing Feature

Quick Overview

Facebook Groups Product Health and Feature Experiment Design

Context

Tasks

Hints

Solution

Submit Your Answer to Earn 20XP

Evaluate Facebook Groups Metrics and Test Comment-Collapsing Feature

Quick Overview

Facebook Groups Product Health and Feature Experiment Design

Context

Tasks

Hints

Solution

Submit Your Answer to Earn 20XP