Statistics
The Foundation
models, predictions, tests, metrics. This section covers the core concepts: probability, distributions, hypothesis testing, and measures of spread and center. If something feels like magic later on, the explanation is probably here.
Bayes' Theorem
intermediateHow to update what you believe when you get new evidence.
Central Limit Theorem
intermediateNo matter what your data looks like, averages tend to behave normally.
Conditional Probability
beginnerHow does knowing one thing change the probability of another?
Correlation vs Causation
beginnerTwo things moving together does not mean one is causing the other.
Hypothesis Testing
beginnerIs this result real, or did it happen by chance?
Mean vs Median vs Mode
beginnerThree ways to describe the center of your data — and why they disagree.
Normal Distribution
beginnerThe bell curve — why so much data naturally clusters around the middle.
Outliers
beginnerThe data points that don't fit — and why they're either your biggest problem or your most important signal.
P-Value & Confidence Intervals
intermediateTwo ways to express how certain you are about a result — and how often they get misread.
Probability Basics
beginnerThe language of uncertainty — how likely is something to happen?
Sampling & Bias
beginnerYour data is only as good as how you collected it.
Variance & Standard Deviation
beginnerHow spread out is your data? Two numbers that measure the same thing — in different units.