Derive Probability of Even Sum in Bernoulli Trials

Quick Overview

This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Derive Probability of Even Sum in Bernoulli Trials states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Derive Probability of Even Sum in Bernoulli Trials

Company: Boston Consulting Group

Role: Data Scientist

Category: Statistics & Math

Difficulty: medium

Interview Round: Take-home Project

##### Scenario Online assessment requires candidates to solve non-formulaic probability puzzles within strict proctoring constraints. ##### Question Let X1,…,Xn be i.i.d. Bernoulli(p). Derive a closed-form expression for P(Σ_i X_i is even). A fair coin is flipped until two consecutive identical outcomes appear. What is the expected number of flips? ##### Hints Use moment-generating tricks or parity arguments for the first; write a recurrence or Markov chain for the second.

Quick Answer: This interview question evaluates statistical assumptions, formulas, estimation strategy, uncertainty, edge cases, and interpretation in a realistic interview setting. A strong answer for Derive Probability of Even Sum in Bernoulli Trials 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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Derive Probability of Even Sum in Bernoulli Trials

Probability Puzzles: Parity and Runs

Context

You are given two independent probability problems commonly seen in data-science take-home assessments. Assume all random variables are defined on the same probability space.

Problems

  1. Parity of a Bernoulli Sum
    • Let X1, ..., Xn be i.i.d. Bernoulli(p). Let S = Σ_{i=1}^n X_i.
    • Derive a closed-form expression for P(S is even).
  2. Waiting Time to First Repeat (Fair Coin)
    • A fair coin is flipped until two consecutive identical outcomes (HH or TT) appear.
    • Find the expected number of flips required.

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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