Design Sampling Strategy to Estimate Fake News Proportion

Quick Overview

Design Sampling Strategy to Estimate Fake News Proportion evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Design Sampling Strategy to Estimate Fake News Proportion

Company: Meta

Role: Data Scientist

Category: Statistics & Math

Difficulty: medium

Interview Round: Onsite

##### Scenario Management is concerned about the volume of fake news on the platform and requests a data-driven report. ##### Question How would you design a sampling strategy to estimate the proportion of fake news on Facebook with confidence intervals? Which user-interaction metrics would you analyze to assess the impact of fake news? ##### Hints Cover random sampling, stratification, estimation of prevalence, significance, and behavioural KPIs such as clicks, shares, dwell-time.

Overview: Design Sampling Strategy to Estimate Fake News Proportion evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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Aug 4, 2025
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Design Sampling Strategy to Estimate Fake News Proportion

Estimating Fake News Prevalence and Impact on Facebook

Context

Management is concerned about the volume and impact of fake news on the platform. You are asked to design a statistically sound approach to:

  • Estimate the proportion of fake news with confidence intervals.
  • Identify and analyze user-interaction metrics that capture the impact of fake news on behavior.

Assume you can sample posts and impressions over a defined time window and obtain ground-truth labels via human review.

Tasks

  1. Sampling Strategy and Estimation
  • Define the target estimand(s) (e.g., proportion of fake news among posts vs among impressions).
  • Propose a random sampling design, including any stratification you would use. Justify strata and allocation.
  • Describe how you would compute prevalence estimates and confidence intervals, accounting for weights and clustering.
  • Include a sample size calculation and how you would handle low-prevalence scenarios.
  1. Impact Assessment via User-Interaction Metrics
  • List the key behavioral KPIs (e.g., clicks, shares, dwell time) you would track to assess impact.
  • Outline the statistical approach to compare fake vs non-fake content (significance testing, regression/matching controls, multiple comparisons).

Clarifying Questions to Ask Guidance

  • Clarify the random variables, distributional assumptions, independence assumptions, and desired output.
  • Show enough derivation for the interviewer to follow the reasoning.
  • Explain how you would validate the result with simulation or sensitivity checks.

What a Strong Answer Covers Guidance

  • A correct setup with definitions, formulas, and boundary conditions.
  • A step-by-step derivation or estimation plan.
  • Interpretation of the result, including uncertainty and practical limitations.
  • Checks for assumptions, edge cases, and numerical stability.

Follow-up Questions Guidance

  • How would the result change if the assumptions were relaxed?
  • Can you verify the answer with a simulation?
  • What is the most likely source of estimation error?
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