Topic hub
Probability & Distributions
Probability rules, from single events to conditional probability, plus the normal, binomial, and Poisson distributions that model random outcomes.
Bayes' Theorem (Bayes' Rule): Formula & Examples
Learn Bayes' rule — the formula for updating probability with new evidence. Worked medical and spam examples, the prior and posterior explained.
Binomial Distribution: Formula, Examples & How to Use It
Understand the binomial distribution formula with worked examples. Calculate exact and cumulative binomial probabilities step by step.
Central Limit Theorem: Definition & Examples
The central limit theorem explains why sample means become normally distributed. Covers the formula, key assumptions, and a fully worked numeric example.
Continuous Distributions: Exponential, Uniform, Gamma & Lognormal
Learn the exponential distribution formula, worked examples, and how the exponential probability distribution compares to uniform, gamma, and lognormal.
Hypergeometric Distribution: Formula & Examples
Learn the hypergeometric distribution formula, see a fully worked example, and understand when to use it instead of the binomial distribution.
Independent, Dependent & Mutually Exclusive Events
Understand independent, dependent, and mutually exclusive events in probability. Worked examples, formulas, and the key distinction explained clearly.
Normal Distribution: The Bell Curve Explained
Understand the normal distribution and its bell curve: the formula, the 68-95-99.7 rule, worked examples, and how to calculate probabilities.
Poisson Distribution: Formula, Examples & When to Use It
Learn the Poisson distribution: its formula, the four conditions, and fully worked examples. Understand when to use it vs binomial and normal distributions.
Probability Distributions Explained: Types, Mean & Examples
A probability distribution maps every outcome to its probability. Learn types, how to calculate the distribution mean, marginal distributions, and Bayesian use.
Probability Formula: How to Calculate Probability Step by Step
Learn the probability formula, conditional probability formula, binomial probability formula, and expected value formula with clear worked examples.
Sampling Distributions: What They Are and Why They Matter
Understand sampling distributions: what they are, how standard error works, the Central Limit Theorem connection, and a fully worked numeric example.
Skewed Distributions: Left Skew, Right Skew & Shape
Learn what a left skewed distribution means, how it compares to a right-skewed distribution, how to measure skewness, and how to identify distribution shape.
What Is the F-Test and F-Distribution in Statistics?
Learn what the F-test and F-distribution are, how the F statistic formula works, and walk through a step-by-step worked ANOVA example.
Z Score Table: How to Read the Normal Distribution Table
Use the z score table to look up cumulative normal probabilities. Step-by-step guide with worked examples, negative z-scores, and an interactive calculator.