Part 9: Statistics & Statistical Inference
From descriptive statistics and probability distributions to hypothesis testing, the bootstrap, Bayesian inference, regression, and experimental design.
Chapters in This Part
Chapter 33: Descriptive Statistics & Distributions
Before you can build a model, run an experiment, or communicate a finding, you need to understand your data. Descriptive statistics and probability di...
Chapter 34: Probability Distributions in Depth
Probability distributions are the foundational language through which we describe uncertainty, model real-world phenomena, and make principled inferen...
Chapter 35: Sampling & Estimation
Statistical inference is the art and science of drawing conclusions about a population from a sample. Every time a pollster surveys 1,000 voters to es...
Chapter 36: Hypothesis Testing
Hypothesis testing is the engine behind most empirical claims in science, engineering, and data-driven product development. At its core, the framework...
Chapter 37: Resampling & the Bootstrap
Classical statistical inference relies on parametric assumptions: data is normally distributed, sample sizes are large enough for the central limit th...
Chapter 38: Bayesian Inference
Bayesian inference offers a fundamentally different philosophy for reasoning under uncertainty than the frequentist methods that dominate introductory...
Chapter 39: Regression & Generalized Linear Models
Regression is the workhorse of statistical modeling. From estimating the effect of a drug dose on blood pressure to predicting housing prices from nei...
Chapter 40: Experimental Design & A/B Testing
Experiments are the gold standard for causal inference. Observational data — no matter how rich, how large, or how cleverly analyzed — can only establ...