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AP Statistics stands for Advanced Placement Statistics. It is a college-level course offered by the College Board to high school students who are interested in studying statistics at an advanced level. The course covers a wide range of topics, including data analysis, sampling, experimentation, and inference.
The course is rigorous and requires students to have a good grasp of algebra. It expects them to engage in data collection, statistical analysis, and interpretation of results, as well as to showcase their understanding of probability and inferential reasoning.
Course Contents
Based on the Understanding by Design® (Wiggins and McTighe) model, this course framework provides a clear and detailed description of the course requirements necessary for student success. The framework specifies what students must know, be able to do, and understand, with a focus on three big ideas that encompass the principles and processes in the discipline of statistics. The framework also encourages instruction that prepares students for advanced coursework in statistics or other fields using statistical reasoning and for active, informed engagement with a world of data to be interpreted appropriately and applied wisely to make informed decisions.
The AP Statistics framework is organized into nine commonly taught units of study that provide one possible sequence for the course. As always, you have the flexibility to organize the course content as you like.
| Unit | Details | Exam Weighting (Multiple-Choice Section) |
|---|---|---|
| Unit 1: Exploring One-Variable Data | You’ll be introduced to how statisticians approach variation and practice representing data, describing distributions of data, and drawing conclusions based on a theoretical distribution | 15%–23% |
| Unit 2: Exploring Two-Variable Data | You’ll build on what you’ve learned by representing two-variable data, comparing distributions, describing relationships between variables, and using models to make predictions | 5%–7% |
| Unit 3: Collecting Data | You’ll be introduced to study design, including the importance of randomization. You’ll understand how to interpret the results of well-designed studies to draw appropriate conclusions and generalizations | 12%–15% |
| Unit 4: Probability, Random Variables, and Probability Distributions | You’ll learn the fundamentals of probability and be introduced to the probability distributions that are the basis for statistical inference | 10%–20% |
| Unit 5: Sampling Distributions | As you build understanding of sampling distributions, you’ll lay the foundation for estimating characteristics of a population and quantifying confidence | 7%–12% |
| Unit 6: Inference for Categorical Data: Proportions | You’ll learn inference procedures for proportions of a categorical variable, building a foundation of understanding of statistical inference, a concept you’ll continue to explore throughout the course | 12%–15% |
| Unit 7: Inference for Quantitative Data: Means | Building on lessons learned about inference in Unit 6, you’ll learn to analyze quantitative data to make inferences about population means | 10%–18% |
| Unit 8: Inference for Categorical Data: Chi-Square | You’ll learn about chi-square tests, which can be used when there are two or more categorical variables | 2%–5% |
| Unit 9: Inference for Quantitative Data: Slopes | You’ll understand that the slope of a regression model is not necessarily the true slope but is based on a single sample from a sampling distribution, and you’ll learn how to construct confidence intervals and perform significance tests for this slope | 2%–5% |
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Mar 10, 2025
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