Question 1

What is the difference between PMF and PDF?

Accepted Answer

PMF (probability mass function) applies to discrete random variables with countable outcomes and gives exact probabilities for specific values. PDF (probability density function) applies to continuous random variables and gives density values, requiring integration over intervals to calculate probabilities.

Question 2

When should I use discrete probability distributions?

Accepted Answer

Use discrete probability distributions when outcomes are countable, such as number of successes, number of events, or any whole number counts. Examples include coin flips, dice rolls, customer arrivals, or defect counts.

Question 3

When should I use continuous probability distributions?

Accepted Answer

Use continuous probability distributions for measurements that can take any value in an interval, such as height, weight, time, temperature, or distance. These variables have infinite possible values within their range.

Question 4

How do probability functions assign probabilities?

Accepted Answer

For discrete variables, the PMF assigns a probability to each specific value, with all probabilities summing to 1. For continuous variables, the PDF assigns density, and probabilities are found by integrating the PDF over intervals.

Question 5

What are the most common probability distributions?

Accepted Answer

Common discrete distributions include Binomial, Poisson, and Geometric. Common continuous distributions include Normal (Gaussian), Exponential, and Uniform. The Normal distribution is the most widely used in statistics.

Probability Functions

What are Probability Functions?

Discrete vs Continuous Distributions

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