Data Science

Probability Distributions Overview

Arun Murali

Tags: data-science; distributions; probability; statistics

🟪 1-Minute Summary

A probability distribution describes how likely different outcomes are for a random variable. Common discrete distributions include binomial (yes/no trials) and Poisson (rare events). Common continuous distributions include normal (bell curve) and uniform (equal probability). Choosing the right distribution is crucial for modeling and hypothesis testing.

🟦 Core Notes (Must-Know)

What is a Probability Distribution?

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Common Discrete Distributions

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Common Continuous Distributions

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How to Choose the Right Distribution

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🟨 Interview Triggers (What Interviewers Actually Test)

Common Interview Questions

  1. “Which distribution would you use to model the number of defects in a manufacturing process?”

    • [Answer: Poisson (rare events)]

  2. “What assumptions does the normal distribution make?”

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🟥 Common Mistakes (Traps to Avoid)

Mistake 1: Assuming all data follows a normal distribution

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🟩 Mini Example (Quick Application)

Scenario

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Solution

import numpy as np
from scipy import stats

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🔗 Related Topics

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