AP StatisticsAP
Exploring data, inference, and probability.
Overview
AP Statistics builds understanding of variability, data collection, probability, and statistical inference.
Why it matters
Teaches data literacy and decision-making under uncertainty used in science, business, and social sciences.
Skills you’ll build
- Graphical summaries
- Study design
- Probability models
- Inference procedures
Topic Breakdown (Units)
Unit 1: Exploring One-Variable Data
- Graphing distributions
- Center & spread
Unit 2: Exploring Two-Variable Data
- Scatterplots
- Correlation
- Least-squares regression
Unit 3: Collecting Data
- Sampling
- Experiment design
Unit 4: Probability, Random Variables, and Probability Distributions
- Rules of probability
- Discrete/continuous RVs
Unit 5: Sampling Distributions
- Central Limit Theorem
- Proportions & means
Unit 6: Inference for Categorical Data: Proportions
- One/two-sample z procedures
Unit 7: Inference for Quantitative Data: Means
- t-procedures for one/two samples
Unit 8: Inference for Categorical Data: Chi-Square
- χ² tests
Unit 9: Inference for Quantitative Data: Slopes
- Inference for regression slope
Lessons & Notes
Unit 1: Exploring One-Variable Data
Describe distributions with numerical and visual tools.
- mean/median
- IQR/SD
Unit 2: Exploring Two-Variable Data
Quantify and model relationships.
- r
- r²
- residuals
Unit 3: Collecting Data
Plan studies and control sources of bias.
- randomization
- blocking
Unit 4: Probability, Random Variables, and Probability Distributions
Model randomness and long-run behavior.
- Law of Large Numbers
- expected value
Unit 5: Sampling Distributions
Understand variability of statistics.
- standard error
- normal approximations
Unit 6: Inference for Categorical Data: Proportions
Test and estimate population proportions.
- conditions
- p̂, z, CI
Unit 7: Inference for Quantitative Data: Means
Test and estimate means.
- t-distribution
- pooled vs. unpooled
Unit 8: Inference for Categorical Data: Chi-Square
Goodness-of-fit, homogeneity, and independence.
- df
- expected counts
Unit 9: Inference for Quantitative Data: Slopes
Model linear relationships with inference.
- t test for β₁
- conditions