AP Statistics

AP Statistics

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

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