Investigate Causes of Decline in Facebook Group Comments

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 Investigate Causes of Decline in Facebook Group Comments states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Investigate Causes of Decline in Facebook Group Comments

Company: Meta

Role: Data Scientist

Category: Analytics & Experimentation

Difficulty: medium

Interview Round: Onsite

##### Scenario Sudden drop in comments per post on Facebook Groups ##### Question Comments per post fell sharply last week. Outline a step-by-step investigation plan, including data cuts, hypotheses, metrics, and follow-up experiments. ##### Hints Structure causes by user, content, product, external; quantify impact.

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 Investigate Causes of Decline in Facebook Group Comments 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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Investigate Causes of Decline in Facebook Group Comments

Scenario

A sharp decline in Comments per Post (CPP) was observed in Facebook Groups last week.

Task

Outline a step-by-step investigation plan to diagnose and address the drop. Your plan should include:

  1. Definitions and sanity checks for the metric.
  2. Key data cuts/segmentations to localize the issue.
  3. A hypotheses tree structured by: user, content, product, and external factors.
  4. Metrics and analyses to validate or falsify each hypothesis, including how to quantify each factor's contribution to the drop.
  5. Follow-up experiments or mitigations, with success metrics and guardrails.

Hints

  • Be explicit about ratio-metric pitfalls (numerator vs denominator) and cohorting choices.
  • Include time comparisons (WoW, DoW, YoY) and seasonality checks.
  • Incorporate experiment flags, app versions, geo/language, group types/sizes, and post types.
  • Quantify impact by segment and roll up contributions to explain the total drop.
  • Propose rapid mitigations (e.g., rollback/ramp holds) when warranted.

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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