AP Statistics Practice

Distributions tell stories about variation. This module introduces the tools for describing one-variable data: shape, center, spread, and the graphs that reveal them.

Two variables often move together. This module explores scatterplots, correlation, and least-squares regression, showing how to quantify and interpret linear relationships.

Good conclusions depend on good data. This module covers sampling methods, experimental design, and the critical distinction between association and causation.

Probability is the language of uncertainty. This module builds the rules of probability, introduces random variables, and explores the normal and binomial models that underpin inference.

Inference bridges the gap between samples and populations. This module covers sampling distributions, confidence intervals, and hypothesis tests for proportions, means, chi-square, and regression slopes.

Parameter versus statistic, sampling variability, simulation, sampling distribution shape, center, spread, normal approximation, and the central limit theorem idea.

One- and two-proportion confidence intervals and significance tests, conditions, p-values, Type I and Type II errors, and power.

One-sample, paired, and two-sample t procedures, robustness, degrees of freedom, conditions, and interpreting conclusions for quantitative data.

Chi-square goodness-of-fit, homogeneity, independence, expected counts, degrees of freedom, and inference for least-squares regression slope.