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This is a medium difficulty Statistics & Math 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 Sampling Strategy to Estimate Fake News Proportion

Quick Overview

This question evaluates skills in survey sampling design, prevalence estimation with weighting and clustering, inferential statistics for low-prevalence events, and impact analysis using behavioral KPIs and comparative methods like regression and matching, and it falls under the Statistics & Math and Data Science domain.

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

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.

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).

Quick Overview

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

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.

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).

Design Sampling Strategy to Estimate Fake News Proportion

Quick Overview

Estimating Fake News Prevalence and Impact on Facebook

Context

Tasks

Solution

Submit Your Answer

Design Sampling Strategy to Estimate Fake News Proportion

Quick Overview

Estimating Fake News Prevalence and Impact on Facebook

Context

Tasks

Solution

Submit Your Answer