Track 6 of 6
James's favoriteStatistics & Probability
From summarizing data all the way to hypothesis testing: descriptive stats, study design, probability and Bayes' theorem, random variables, the binomial and normal distributions, the Central Limit Theorem, confidence intervals, and significance tests. The math behind science, polls, and AI.
13 lessons · ~155 min total · 26-question quiz
Lessons
- 01
Descriptive Statistics
Mean, median, mode, range, variance, and standard deviation.
- 02
Data Displays & Distribution Shape
Histograms, box plots, quartiles, the IQR, and spotting skew and outliers.
- 03
Sampling & Study Design
Populations vs samples, random sampling methods, bias, and experiments vs observational studies.
- 04
Probability Basics
Sample spaces, independence, conditional probability, expected value.
- 05
Conditional Probability & Bayes' Theorem
Updating probabilities when you learn something new — and why base rates matter.
- 06
Random Variables & Expected Value
Probability distributions, expected value, and the variance of a random variable.
- 07
The Normal Distribution
The bell curve, the 68-95-99.7 rule, and z-scores.
- 08
Permutations & Combinations
Counting arrangements when order matters — and when it doesn't.
- 09
The Binomial Distribution
Counting successes in n independent trials, with a clean formula for mean and spread.
- 10
Sampling Distributions & the Central Limit Theorem
Why sample means are predictable — the engine behind all of inference.
- 11
Confidence Intervals
Estimating a parameter with a margin of error — and what 'confidence' really means.
- 12
Hypothesis Testing
Null vs alternative, test statistics, p-values, and the two ways to be wrong.
- 13
Linear Regression & Correlation
Best-fit lines, the correlation coefficient, and the causation trap.
Section quiz
26 questions covering the lessons above. Instant feedback on each, full review at the end.
Start the quiz