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## About the Course

Statistics are a part of everyday life. You can see them in AI models, news polls, popular music rankings, and medical research. Discover how the statistics you see every day are developed and learn how to evaluate their credibility for yourself in AP Statistics.

## 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 Date

## About the Units

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

**On The Exam**

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

**On The Exam**

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

**On The Exam**

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

**On The Exam**

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

**On The Exam**

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

**On The Exam**

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

**On The Exam**

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

**On The Exam**

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

**On The Exam**

2%–5% of Score

Credit and Placement

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## Course Resources

## See Where AP Can Take You

AP Statistics can lead to a wide range of careers and college majors