Probability Distributions
Normal distribution, binomial distribution, and expected value
Start with the basics and progress through 6 lessons. Each lesson builds on the previous one.
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10 questions, new mix each time (from 96)
In This Topic (6 lessons)
Introduction to Probability Distributions
Learn what probability distributions are and how they describe the likelihood of different outcomes.
Binomial Probability
Learn to calculate probabilities when an experiment has exactly two outcomes and is repeated multiple times.
Discrete vs Continuous Distributions
Learn the difference between discrete and continuous probability distributions, and when to use each type.
Expected Value
Learn how to calculate the average outcome of a random event over many trials.
Introduction to Normal Distribution
Learn about the bell curve, the most important probability distribution in statistics.
Binomial Distribution
Learn how to calculate probabilities for repeated independent trials with two outcomes.
Probability distributions describe how probabilities are spread across possible outcomes. From discrete distributions like binomial to continuous distributions like normal, these models are essential for statistics and data science.
What You'll Learn
- Understand discrete vs continuous distributions
- Apply the binomial distribution
- Understand the normal distribution
- Use z-scores for normal distributions
- Apply distributions to real problems
Frequently Asked Questions
What is a probability distribution?
A function that gives the probability of each possible outcome. The sum (discrete) or integral (continuous) of all probabilities is 1.
What is the normal distribution?
The bell curve, symmetric about the mean. About 68% of data falls within 1 standard deviation, 95% within 2, and 99.7% within 3.
What is a z-score?
The number of standard deviations a value is from the mean: z = (x - μ)/σ. It lets you compare values from different normal distributions.