AP Statistics - Study Guides, Flashcards, AP-style Practice & Mock Exams
This complete AP Statistics course is your central hub for effective AP Statistics exam prep. Systematically work through all required units and topics, then apply your skills using a wide array of practice materials to sharpen your analytical abilities for the exam.
Course Overview
This course introduces the major concepts for collecting, analyzing, and drawing conclusions from data. The curriculum is structured around four key themes: exploring data, sampling and experimentation, anticipating patterns, and statistical inference. Students will learn to describe patterns, plan studies, and use probability and simulation to understand random phenomena. A significant portion of the course is dedicated to statistical inference, including the construction of confidence intervals and the execution of hypothesis testing. The course also prepares students for the exam format, which includes both multiple-choice and free-response questions, and addresses the specific calculator policy for performing complex analyses like regression.
To prepare for the exam, this course is organized into nine sequential units. Students progress by mastering individual topics, each followed by an AP-style quiz to reinforce learning. At the end of each unit, a comprehensive exam serves as a progress check, highlighting areas that may require targeted review. This cycle of topic-level learning and unit-level assessment builds a strong foundation. The course provides over 500 practice questions and culminates in three full-length mock exams, allowing students to simulate the testing environment, refine their pacing, and solidify their understanding of all course material.
Units & Topics
Unit 1: Exploring One-Variable Data
expand_more
We will use graphical and numerical tools to describe, summarize, and compare data distributions, establishing the fundamental principles required for statistical inference.
- 1.0Unit Overview
- 1.1Introducing Statistics: What Can We Learn from Data?
- 1.2The Language of Variation: Variables
- 1.3Representing a Categorical Variable with Tables
- 1.4Representing a Categorical Variable with Graphs
- 1.5Representing a Quantitative Variable with Graphs
- 1.6Describing the Distribution of a Quantitative Variable
- 1.7Summary Statistics for a Quantitative Variable
- 1.8Graphical Representations of Summary Statistics
- 1.9Comparing Distributions of a Quantitative Variable
- 1.10The Normal Distribution
- 1.11Unit Exam
Unit 2: Exploring Two-Variable Data
expand_more
We will investigate bivariate data to describe associations between variables, using regression to model linear relationships and analyze the resulting prediction errors.
- 2.0Unit Overview
- 2.1Introducing Statistics: Are Variables Related?
- 2.2Representing Two Categorical Variables
- 2.3Statistics for Two Categorical Variables
- 2.4Representing the Relationship Between Two Quantitative Variables
- 2.5Correlation
- 2.6Linear Regression Models
- 2.7Residuals
- 2.8Least Squares Regression
- 2.9Analyzing Departures from Linearity
- 2.10Unit Exam
Unit 3: Collecting Data
expand_more
We will examine methods for collecting reliable data, contrasting various sampling strategies with the principles of well-designed experiments to answer statistical questions truthfully.
- 3.0Unit Overview
- 3.1Introducing Statistics: Do the Data We Collected Tell the Truth?
- 3.2Introduction to Planning a Study
- 3.3Random Sampling and Data Collection
- 3.4Potential Problems with Sampling
- 3.5Introduction to Experimental Design
- 3.6Selecting an Experimental Design
- 3.7Inference and Experiments
- 3.8Unit Exam
Unit 4: Probability, Random Variables, and Probability Distributions
expand_more
We will explore the rules of probability and random variables, building the essential theoretical foundation for future work with statistical inference.
- 4.0Unit Overview
- 4.1Introducing Statistics: Random and Non-Random Patterns?
- 4.2Estimating Probabilities Using Simulation
- 4.3Introduction to Probability
- 4.4Mutually Exclusive Events
- 4.5Conditional Probability
- 4.6Independent Events and Unions of Events
- 4.7Introduction to Random Variables and Probability Distributions
- 4.8Mean and Standard Deviation of Random Variables
- 4.9Combining Random Variables
- 4.10Introduction to the Binomial Distribution
- 4.11Parameters for a Binomial Distribution
- 4.12The Geometric Distribution
- 4.13Unit Exam
Unit 5: Sampling Distributions
expand_more
This unit builds the theoretical foundation for statistical inference by examining the predictable, long-run patterns that emerge when we repeatedly sample from a population.
- 5.0Unit Overview
- 5.1Introducing Statistics: Why Is My Sample Not Like Yours?
- 5.2The Normal Distribution, Revisited
- 5.3The Central Limit Theorem
- 5.4Biased and Unbiased Point Estimates
- 5.5Sampling Distributions for Sample Proportions
- 5.6Sampling Distributions for Differences in Sample Proportions
- 5.7Sampling Distributions for Sample Means
- 5.8Sampling Distributions for Differences in Sample Means
- 5.9Unit Exam
Unit 6: Inference for Categorical Data: Proportions
expand_more
This unit introduces formal statistical inference, using confidence intervals and significance tests to make and justify claims about one or two population proportions.
- 6.0Unit Overview
- 6.1Introducing Statistics: Why Be Normal?
- 6.2Constructing a Confidence Interval for a Population Proportion
- 6.3Justifying a Claim Based on a Confidence Interval for a Population Proportion
- 6.4Setting Up a Test for a Population Proportion
- 6.5Interpreting p-Values
- 6.6Concluding a Test for a Population Proportion
- 6.7Potential Errors When Performing Tests
- 6.8Confidence Intervals for the Difference of Two Proportions
- 6.9Justifying a Claim Based on a Confidence Interval for a Difference of Population Proportions
- 6.10Setting Up a Test for the Difference of Two Population Proportions
- 6.11Carrying Out a Test for the Difference of Two Population Proportions
- 6.12Unit Exam
Unit 7: Inference for Quantitative Data: Means
expand_more
We will extend our understanding of statistical inference by constructing estimates and testing claims for both a single population mean and the difference between two.
- 7.0Unit Overview
- 7.1Introducing Statistics: Should I Worry About Error?
- 7.2Constructing a Confidence Interval for a Population Mean
- 7.3Justifying a Claim About a Population Mean Based on a Confidence Interval
- 7.4Setting Up a Test for a Population Mean
- 7.5Carrying Out a Test for a Population Mean
- 7.6Confidence Intervals for the Difference of Two Means
- 7.7Justifying a Claim About the Difference of Two Means Based on a Confidence Interval
- 7.8Setting Up a Test for the Difference of Two Population Means
- 7.9Carrying Out a Test for the Difference of Two Population Means
- 7.10Skills Focus: Selecting, Implementing, and Communicating Inference Procedures
- 7.11Unit Exam
Unit 8: Inference for Categorical Data: Chi-Square
expand_more
We will use chi-square procedures for hypothesis testing to determine if observed categorical data distributions differ significantly from what was expected or show an association.
- 8.0Unit Overview
- 8.1Introducing Statistics: Are My Results Unexpected?
- 8.2Setting Up a Chi-Square Goodness of Fit Test
- 8.3Carrying Out a Chi-Square Test for Goodness of Fit
- 8.4Expected Counts in Two-Way Tables
- 8.5Setting Up a Chi-Square Test for Homogeneity or Independence
- 8.6Carrying Out a Chi-Square Test for Homogeneity or Independence
- 8.7Skills Focus: Selecting an Appropriate Inference Procedure for Categorical Data
- 8.8Unit Exam
Unit 9: Inference for Quantitative Data: Slopes
expand_more
We will extend our understanding of linear relationships by applying statistical inference to make conclusions about the true slope of a population regression line.
- 9.0Unit Overview
- 9.1Introducing Statistics: Do Those Points Align?
- 9.2Confidence Intervals for the Slope of a Regression Model
- 9.3Justifying a Claim About the Slope of a Regression Model Based on a Confidence Interval
- 9.4Setting Up a Test for the Slope of a Regression Model
- 9.5Carrying Out a Test for the Slope of a Regression Model
- 9.6Skills Focus: Selecting an Appropriate Inference Procedure
- 9.7Unit Exam
Frequently Asked Questions
What is the format of the AP Statistics exam?
expand_more
The exam has two 90-minute sections of equal weight. You will first complete a 40-question multiple-choice section, followed by a six-question free-response section that requires you to communicate statistical methods, definitions, and conclusions effectively.
How should I use this platform to study for AP Statistics?
expand_more
We recommend a sequential approach to master the ~31 hours of content. Work through the 9 units and 80 topics, then test your knowledge with AP-style quizzes and unit exams before attempting the full-length mock exams to gauge your readiness.
What is the calculator policy for the AP Statistics exam?
expand_more
A graphing calculator is required for both the multiple-choice and free-response sections. You should be proficient with its functions for statistical calculations, such as finding regression models, and for creating graphical displays like histograms or scatterplots.
What types of questions are in the Free-Response (FRQ) section?
expand_more
The FRQ section contains six questions to be answered in 90 minutes. It includes five standard free-response questions and one longer, more complex "Investigative Task" designed to assess your ability to integrate multiple skills and concepts.
What are the main skills I will develop in this course?
expand_more
You will learn to collect, analyze, and draw conclusions from data. Key skills include describing patterns, planning and conducting studies using proper sampling techniques, exploring random phenomena using probability, and employing statistical inference to justify conclusions.
Do I need to memorize all the formulas for the exam?
expand_more
No, you will be provided with a formula sheet and statistical tables. This resource includes key formulas for probability, descriptive statistics, and inference procedures like confidence intervals and hypothesis testing, allowing you to focus on application.
What is statistical inference and why is it important?
expand_more
Statistical inference is the practice of using data from a sample to draw conclusions about a larger population. This is a core component of the course, encompassing two primary methods you will master: constructing confidence intervals and performing hypothesis testing.
How does this course prepare me for the exam questions?
expand_more
This course provides extensive practice to build your confidence and skills. You can test your understanding with 523 practice questions and 1087 flashcards, then simulate the exam experience with three full-length mock exams designed to mirror the official test.
What is regression and how is it used in AP Statistics?
expand_more
Regression is a statistical method used to model and analyze the relationship between a response variable and one or more explanatory variables. In this course, you will learn to create and interpret linear regression models, assess their fit, and use them for prediction.
What are the major themes covered in AP Statistics?
expand_more
The course is organized around four major conceptual themes. You will progress from exploring data and designing studies through sampling, to understanding probability and randomness, and finally to making data-driven conclusions using statistical inference across the course's nine units.
Ready to study smarter for AP Statistics?
Get instant access to all study materials, practice questions, and mock exams. Join thousands of students mastering AP Statistics with PrepGo.