# AP Statistics

Learn about the major concepts and tools used for collecting, analyzing, and drawing conclusions from data. You’ll explore statistics through discussion and activities, and you'll design surveys and experiments.

### Skills You'll Learn

• Selecting methods for collecting or analyzing data

• Describing patterns, trends, associations, and relationships in data

• Using probability and simulation to describe probability distributions and define uncertainty in statistical inference

• Using statistical reasoning to draw appropriate conclusions and justify claims

### Equivalency and Prerequisites

#### College Course Equivalent

A one-semester, introductory, non-calculus-based college course in statistics

#### Recommended Prerequisites

A second-year course in algebra

### Exam Dates

• Thu, May 13, 2021,
12 PM Local

### AP Statistics Exam

This is the regularly scheduled date for the AP Statistics Exam.

The course content outlined below is organized into commonly taught units of study that provide one possible sequence for the course. Your teacher may choose to organize the course content differently based on local priorities and preferences.

## Course Content

### 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.

Topics may include:

• Variation in categorical and quantitative variables
• Representing data using tables or graphs
• Calculating and interpreting statistics
• Describing and comparing distributions of data
• The normal distribution

15%–23% of Score

### 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.

Topics may include:

• Comparing representations of 2 categorical variables
• Calculating statistics for 2 categorical variables
• Representing bivariate quantitative data using scatter plots
• Describing associations in bivariate data and interpreting correlation
• Linear regression models
• Residuals and residual plots
• Departures from linearity

5%–7% of Score

### 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.

Topics may include:

• Planning a study
• Sampling methods
• Sources of bias in sampling methods
• Designing an experiment
• Interpreting the results of an experiment

12%–15% of Score

### 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.

Topics may include:

• Using simulation to estimate probabilities
• Calculating the probability of a random event
• Random variables and probability distributions
• The binomial distribution
• The geometric distribution

10%–20% of Score

### 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.

Topics may include:

• Variation in statistics for samples collected from the same population
• The central limit theorem
• Biased and unbiased point estimates
• Sampling distributions for sample proportions
• Sampling distributions for sample means

7%–12% of Score

### 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.

Topics may include:

• Constructing and interpreting a confidence interval for a population proportion
• Setting up and carrying out a test for a population proportion
• Interpreting a p-value and justifying a claim about a population proportion
• Type I and Type II errors in significance testing
• Confidence intervals and tests for the difference of 2 proportions

12%–15% of Score

### 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.

Topics may include:

• Constructing and interpreting a confidence interval for a population mean
• Setting up and carrying out a test for a population mean
• Interpreting a p-value and justifying a claim about a population mean
• Confidence intervals and tests for the difference of 2 population means

10%–18% of Score

### 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.

Topics may include:

• The chi-square test for goodness of fit
• The chi-square test for homogeneity
• The chi-square test for independence
• Selecting an appropriate inference procedure for categorical data

2%–5% of Score

### 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.

Topics may include:

• Confidence intervals for the slope of a regression model
• Setting up and carrying out a test for the slope of a regression model
• Selecting an appropriate inference procedure

2%–5% of Score