Analyze Comment Distribution Using Statistical Metrics and Tests
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 Analyze Comment Distribution Using Statistical Metrics and Tests states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analyze Comment Distribution Using Statistical Metrics and Tests
Company: Meta
Role: Data Scientist
Category: Analytics & Experimentation
Difficulty: medium
Interview Round: Onsite
##### Scenario
You are analysing comment activity on a social media app to understand whether engagement is evenly spread across posts or dominated by a few viral ones.
##### Question
How would you quantitatively evaluate the distribution of comments per post? Which metrics or statistical tests would you choose, why, and what hypotheses would you formulate?
##### Hints
Consider Gini, Lorenz curve, power-law fit, chi-square or KS tests versus theoretical distribution.
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 Analyze Comment Distribution Using Statistical Metrics and Tests states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Analyze Comment Distribution Using Statistical Metrics and Tests
Assessing Concentration of Comments Across Posts
Scenario
You are analyzing comments on a social media app. Each post i accrues a number of comments C_i over a fixed window (e.g., first 24 hours after posting). You want to know whether engagement is evenly distributed across posts or dominated by a small fraction of viral posts.
Task
Propose a quantitative approach to evaluate the distribution of comments per post:
Define the core variable(s) you will analyze and any normalizations (e.g., time window, exposure-adjusted rates).
Specify descriptive and concentration metrics you will compute, and why they are informative (e.g., Lorenz curve, Gini coefficient, top-k share).
Describe distributional models you would consider (e.g., Poisson, Negative Binomial, zero-inflated models, lognormal/power-law tails) and how you would check fit.
Choose statistical tests to assess goodness-of-fit and to compare distributions across cohorts or time.
Formulate clear hypotheses that distinguish "evenly spread" from "dominated by few" and how you would test them.
Assume you have large sample sizes and can segment by country, surface, or time period as needed.
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?