Concepts
Intuition-first explainers grouped by the kind of question they answer — not by classroom difficulty.
Histograms and Distribution Shapes
How to read the shape of a dataset — and why identical summary statistics can hide wildly different data.
Mean vs. Median
Why the average can be a misleading summary — and when to trust the middle value instead.
Sampling
How a poll of 1,000 people can reliably represent 330 million — and why it breaks the moment sampling stops being random.
Standard Deviation and Variance
Why spread matters as much as the average, and how to measure it.
The Normal Distribution
Why so many things in nature cluster around a middle value — and how to read the bell curve.
Trend vs. Noise
How to tell the difference between a real pattern and what random variation naturally looks like over short windows.
The Central Limit Theorem
Why averages of random samples tend toward a normal distribution — and how this single fact makes all of classical statistics possible.
Confidence Intervals
What '95% confident' actually means — and why the most common interpretation is precisely backwards.
Effect Size
Statistical significance flags an effect unlikely to be pure noise. Effect size tells you whether it's big enough to matter.
Multiple Comparisons
Why running enough statistical tests guarantees false positives — and what to do about it.
P-Values
The most cited number in science is also the most misunderstood. Here is what p < 0.05 actually means — and what it doesn't.
Base Rate Neglect
Why a 99%-accurate positive test can still be mostly wrong — and why our intuition about probability is systematically broken.
Random Walks
Why sequences generated by pure chance look indistinguishable from meaningful trends — and what that tells us about markets and patterns.
The Monty Hall Problem
Why switching doors doubles your odds — and what a game show reveals about how information changes probability.
Bayes' Theorem
How a positive test result can still be mostly wrong — and how to update beliefs correctly when evidence arrives.
Correlation vs. Causation
Why two things moving together doesn't mean one causes the other — and how to tell the difference.
Survivorship Bias
Why the data that reaches you is never the full story — and how the missing failures quietly corrupt every conclusion you draw from winners.
Confounding Variables
Why the variable you're not measuring is often the one driving the result — and how to defend against it.
Overfitting
Why a model that gets everything right on training data is probably wrong — and how to build models that generalize.
Regression Intuition
What a regression line actually is, why it's the best line, and what R² is really telling you.