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Compare Normal and Poisson Distributions in Statistics

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of probability distributions by comparing Normal and Poisson in terms of support, parameters, shape, and the derivation of their means and variances, as well as assessing when a discrete count distribution can be approximated by a continuous one.

  • medium
  • Apple
  • Statistics & Math
  • Data Scientist

Compare Normal and Poisson Distributions in Statistics

Company: Apple

Role: Data Scientist

Category: Statistics & Math

Difficulty: medium

Interview Round: Technical Screen

##### Scenario You are modeling event counts versus continuous measurements and must choose the appropriate statistical distribution. ##### Question Explain the main differences between the Normal and Poisson distributions. Under what conditions can a Poisson distribution be approximated by a Normal distribution? Derive the mean and variance for each. ##### Hints Focus on support, parameters, shape, CLT conditions, λ→∞ approximation.

Quick Answer: This question evaluates understanding of probability distributions by comparing Normal and Poisson in terms of support, parameters, shape, and the derivation of their means and variances, as well as assessing when a discrete count distribution can be approximated by a continuous one.

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Apple
Jul 12, 2025, 6:59 PM
Data Scientist
Technical Screen
Statistics & Math
25
0

Modeling Counts vs. Continuous Measurements

Scenario

You are modeling event counts (e.g., number of clicks) versus continuous measurements (e.g., response time). You need to choose an appropriate distribution and understand when one can approximate the other.

Question

  • Explain the main differences between the Normal and Poisson distributions (support, parameters, shape, typical use cases).
  • Under what conditions can a Poisson distribution be approximated by a Normal distribution? State rules of thumb and any corrections.
  • Derive the mean and variance for each distribution.

Hints: support, parameters, shape, CLT conditions, λ → ∞ approximation.

Solution

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