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Determine Probability of Friend Request Being Fake

Last updated: Mar 29, 2026

Quick Overview

Determine Probability of Friend Request Being Fake 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.

  • easy
  • Meta
  • Statistics & Math
  • Data Scientist

Determine Probability of Friend Request Being Fake

Company: Meta

Role: Data Scientist

Category: Statistics & Math

Difficulty: easy

Interview Round: Onsite

##### Scenario Probability that an incoming friend request is from a fake account ##### Question 95% of accounts are real, 5% fake; fake accounts send 10× more friend requests than real ones. If you receive a friend request, what is the probability it is from a fake account? ##### Hints Apply Bayes’ theorem: posterior ∝ prior × likelihood.

Quick Answer: Determine Probability of Friend Request Being Fake 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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|Home/Statistics & Math/Meta

Determine Probability of Friend Request Being Fake

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Meta
Aug 4, 2025, 10:55 AM
easyData ScientistOnsiteStatistics & Math
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Determine Probability of Friend Request Being Fake

Scenario

You operate a platform where 95% of accounts are real and 5% are fake. Fake accounts send 10 times as many friend requests per account as real accounts.

Question

If you receive a random incoming friend request, what is the probability that it is from a fake account?

Assumptions

  • "10× more" means each fake account sends friend requests at a per-account rate 10 times that of a real account.
  • You are equally likely to receive any given friend request (ignoring network structure or targeting effects).

Hint

Apply Bayes’ theorem: posterior ∝ prior × likelihood.

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 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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