The language of data and uncertainty. Start here before anything else — these are the tools that underpin all of machine learning, econometrics, and data science.
Mean, median, variance, standard deviation, covariance, correlation, normal distribution, t-distribution, p-values, and confidence intervals — with interactive charts.
Null and alternative hypotheses, test statistics, critical values, and type I/II errors — structured framework for drawing conclusions from data.
Find parameters that make your observed data most probable. The foundation of most modern statistical estimation.
How much information does one observation carry about a parameter? The Cramér-Rao bound and why it matters for estimation efficiency.