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Compute expected coin flips to meet on octagon

Last updated: Mar 29, 2026

Quick Overview

Compute expected coin flips to meet on octagon 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.

  • medium
  • Optiver
  • Statistics & Math
  • Data Scientist

Compute expected coin flips to meet on octagon

Company: Optiver

Role: Data Scientist

Category: Statistics & Math

Difficulty: medium

Interview Round: Technical Screen

Two independent particles start at opposite vertices (four edges apart) of a regular octagon. At each time step, flip one fair coin for each particle: heads moves one step clockwise, tails one step counterclockwise. What is the expected total number of coin flips required until the two particles occupy the same vertex?

Quick Answer: Compute expected coin flips to meet on octagon 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/Optiver

Compute expected coin flips to meet on octagon

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Optiver
Jul 17, 2025, 12:00 AM
mediumData ScientistTechnical ScreenStatistics & Math
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0

Compute expected coin flips to meet on octagon

Meeting Time on a Random Walk Around an Octagon

Problem

Two independent particles start at opposite vertices (four edges apart) of a regular octagon. At each discrete time step, each particle flips a fair coin: heads moves one step clockwise; tails moves one step counterclockwise. Two coins are flipped per time step (one for each particle).

What is the expected total number of coin flips required until the two particles occupy the same vertex?

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

  • 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

  • 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

  • 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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