Your Guide to the 2024 AP Statistics Exam

We know that studying for your AP exams can be stressful, but Fiveable has your back! We created a study plan to help you crush your AP Statistics exam. This guide will continue to update with information about the 2024 exams, as well as helpful resources to help you do your best on test day. Unlock Cram Mode for access to our cram events—students who have successfully passed their AP exams will answer your questions and guide your last-minute studying LIVE! And don't miss out on unlimited access to our database of thousands of practice questions. 

Format of the 2024 AP Statistics Exam

Going into test day, this is the format to expect: 

Section 1: Multiple Choice (50% of your score)

• 40 questions in 90 minutes

Section 2: Free Response (50% of your score)

• 6 questions in 90 minutes
• Part A: 65 mins
  • 1 multipart question with a focus on collecting data
  • 1 multipart question with a focus on exploring data
  • 1 multipart question with a focus on probability and sampling distributions
  • 1 multipart question with a focus on inference
  • 1 multipart question that combines 2 or more skill categories
• Part B: 25 mins
  • 1 investigative task that assesses multiple skill categories and content areas

👉 Check out the 2023 AP Statistics Free-Response Section posted on the College Board site.

When is the 2024 AP Statistics exam and how do I take it?

**The exam is on paper, in school, on Tuesday, May 7, 2024, at 12:00 pm, your local time. **

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How should I prepare for the exam?

• • First, download the AP Statistics Cheatsheet PDF - a single sheet that covers everything you need to know at a high level. Take note of your strengths and weaknesses!
• We've put together the study plan found below to help you study between now and May. This will cover all of the units and essay types to prepare you for your exam. Pay special attention to the units that you need the most improvement in.
• Study, practice, and review for test day with other students during our live cram sessions via Cram Mode. Cram live streams will teach, review, and practice important topics from AP courses, college admission tests, and college admission topics. These streams are hosted by experienced students who know what you need to succeed.

Pre-Work: Set Up Your Study Environment

Before you begin studying, take some time to get organized.

🖥 Create a study space.

Make sure you have a designated place at home to study. Somewhere you can keep all of your materials, where you can focus on learning, and where you are comfortable. Spend some time prepping the space with everything you need and you can even let others in the family know that this is your study space. 

📚 Organize your study materials.

Get your notebook, textbook, prep books, or whatever other physical materials you have. Also, create a space for you to keep track of review. Start a new section in your notebook to take notes or start a Google Doc to keep track of your notes. Get yourself set up!

📅 Plan designated times for studying.

The hardest part about studying from home is sticking to a routine. Decide on one hour every day that you can dedicate to studying. This can be any time of the day, whatever works best for you. Set a timer on your phone for that time and really try to stick to it. The routine will help you stay on track.

🏆 Decide on an accountability plan.

How will you hold yourself accountable to this study plan? You may or may not have a teacher or rules set up to help you stay on track, so you need to set some for yourself. First, set your goal. This could be studying for x number of hours or getting through a unit. Then, create a reward for yourself. If you reach your goal, then x. This will help stay focused!

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AP Statistics 2024 Study Plan

👆 Unit 1: Exploring One-Variable Data

Big takeaways:

Unit 1 is about creating and analyzing graphs of data. This includes both categorical and quantitative data. For categorical data, we should be able to read and create tables and bar graphs and calculate proportions/percentages. For quantitative data, we should be able to read and create dot plots, stemplots, histograms, and boxplots. We should also be able to describe the shape, center, variability (spread), and any unusual features of a distribution of quantitative data. This includes making calculations such as mean, median, range, interquartile range (IQR), and standard deviation. Our descriptions and calculations can be used to compare data from multiple groups. Finally, Unit 1 ends with describing the position of individuals within a quantitative data set, including using percentiles and z-scores. This leads us to an initial exploration of the Normal Distribution, though we will study that more in-depth in Units 4-5.

Definitely do this:

📚 Read these study guides:

• 1.0 Unit 1 Overview

• 1.1 Introducing Statistics: What Can We Learn From Data?

• 1.2 The Language of Variation: Variables

• 1.3 Representing a Categorical Variable with Tables

• 1.4 Representing a Categorical Variable with Graphs

• 1.5 Representing a Quantitative Variable with Graphs

• 1.6 Describing the Distribution of a Quantitative Variable

• 1.7 Summary Statistics for a Quantitative Variable

• 1.8 Graphical Representations of Summary Statistics

• 1.9 Comparing Distributions of a Quantitative Variable

• 1.10 The Normal Distribution 🎥 Watch these videos from the Fiveable archives:

• Analyzing Categorical Variables: An intro to some key terms and graphs (use first 15 minutes)

• Describing Data in a Distribution: A breakdown of percentile a cumulative graphs

• Normal Distributions: A good intro to all things Normal!

📰 Check out these articles: 

• Relative Dominance: A real-life example of how z-scores can help compare individuals from different distributions, using golfers (source: Grantland)

✍️ Practice:

• Practice an AP-Style Problem: check out this post and practice your free-response skills!
• Displaying Quantitative Data with Graph

If you have more time or want to dig deeper:

• 💎 Check out some online applets:- Mean vs. Median interactive applet: Play with this applet to get a sense of how changing different data values impacts the mean and median
• Normal Distribution applet: A visual of the Standard Normal Curve. Update the mean and standard deviation to look at any data set.

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✌️ Unit 2: Exploring Two-Variable Data

Big takeaways:

Unit 2 is about creating and analyzing graphs of data when two variables are measured about each individual in a data set. For categorical data, we should be able to read and create two-way tables or segmented bar graphs and calculate conditional percentages. These can be used to comment on the association (or lack thereof) between the two variables. For quantitative data, we should be able to read, create, and describe scatterplots, which can also be used to comment on the apparent association between two variables.

The second half of Unit 2 is then focused on linear regression, a process by which we can make predictions about one quantitative variable (a response variable) using another (an explanatory variable). We should be able to use Least-Squares Regression Lines to make these predictions, and interpret several components of the LSRLs (including slope, intercept, and other calculated values such as s or r2)

Definitely do this:

📚 Read these study guides:

• 2.0 Unit 2 Overview
• 2.1 Introducing Statistics: Are Variables Related?
• 2.2 Representing Two Categorical Variables
• 2.3 Statistics for Two Categorical Variables
• 2.4 Representing the Relationship Between Two Quantitative Variables
• 2.5 Correlation
• 2.6 Linear Regression Models
• 2.7 Residuals
• 2.8 Least Squares Regression
• 2.9 Analyzing Departures from Linearity

🎥 Watch these videos from the Fiveable archives:

• Analyzing Categorical Variables: Start at 14:38 for an example of two-way tables and stay for segmented bar graphs
• Describing Scatterplots & Association: How to describe the direction, strength, and form of an association, as well as an introduction to the correlation coefficient r
• Using Least-Squares Regression Lines: How to make predictions from regression lines and calculating residuals
• Advanced Linear Regression: Interpreting “s”, “r2”, and reading computer outputs of regression data

✍️ Practice:

• Practice an AP-Style Problem: check out this post and practice your free-response skills!

If you have more time or want to dig deeper:

💎 Check out some online applets:

• Least-Squares Regression: Try to guess the least-squares regression line from a scatterplot of data 😀  Just for fun!

• Spurious Correlations: Data sets with very high “r” values that… well… you’ll see... [Source: Tyler Vigen]

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🔎 Unit 3: Collecting Data

Big takeaways:

While Units 1-2 were about graphing and analyzing sets of data, Unit 3 is about examining the methods through which we can collect that data. For sample surveys, we should be able to describe various methods of selecting samples, particularly the random methods (simple random, stratified random, cluster, and systematic samples). However, not all samples are collected through a random process, and we should be prepared to discuss possible sources of bias in surveys (including via non-random selection processes).

We then turn to the differences between observational studies and experiments, and the features of a well-designed experiment. We should be able to define many common terms associated with experiments (many of which you’ve likely seen in other courses!), and compare and contrast several common experimental designs: completely randomized design, randomized block design, and matched-pairs design.

Definitely do this:

📚 Read these study guides:

• 3.0 Unit 3 Overview
• 3.1 Introducing Statistics: Do the Data We Collected Tell the Truth?
• 3.2 Introduction to Planning a Study
• 3.3 Random Sampling and Data Collection
• 3.4 Potential Problems with Sampling
• 3.5 Introduction to Experimental Design
• 3.6 Selecting an Experimental Design
• 3.7 Inference and Experiments

🎥 Watch these videos from the Fiveable archives:

• Sampling Methods and Sources of Bias: A breakdown of the different ways we can take samples, and how to talk about bias on the AP exam.
• Experiments and Observational Studies: All things experiments! Includes a discussion of the possible pitfalls of observational studies (confounding)

✍️ Practice:

• AP-Style Problem #1: a practice question on surveys and sampling methods.
• AP-Style Problem #2: a practice question on observational studies/experiments

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🎲 Unit 4: Probability & Random Variables

Big takeaways:

Unit 4 is where AP Statistics gets “math-y,” with lots of calculations and formulas. We are asked to calculate or interpret probabilities in a variety of settings, beginning with the understanding that probability reflects what we should expect to occur over the long run. We should be able to design and execute simulations for a given scenario - and then the calculations begin. We should be able to calculate the probability of multiple events using a variety of strategies (including Two-Way Tables, Tree Diagrams, and/or Venn Diagrams).

We should also be able to categorize different events as “mutually exclusive” or “independent,” with justification. Conditional probability [P(A | B)] plays a big role in this part of the unit. Shifting over to random variables, we should be able to calculate the mean (expected value) or standard deviation of a random variable, and combine them using similar rules to Unit 1. We conclude Unit 4 with a look at Binomial and Geometric random variables, which are two special types of variables that arise frequently in applications.

Definitely do this:

📚 Read these study guides:

• 4.0 Unit 4 Overview
• 4.1 Introducing Statistics: Random and Non-Random Patterns?
• 4.2 Estimating Probabilities Using Simulation
• 4.3 Introduction to Probability
• 4.4 Mutually Exclusive Events
• 4.5 Conditional Probability
• 4.6 Independent Events and Unions of Events
• 4.7 Introduction to Random Variables and Probability Distributions
• 4.8 Mean and Standard Deviation of Random Variables
• 4.9 Combining Random Variables
• 4.10 Introduction to the Binomial Distribution
• 4.11 Parameters for a Binomial Distribution
• 4.12 The Geometric Distribution

🎥 Watch these videos from the Fiveable archives:

• Randomness & Simulation: Explore some definitions (and myths) about probability and randomness
• Basic Probability Rules: A breakdown of commonly-tested probability rules, using Two-Way Tables for most scenarios
• Random Variables & Binomial/Geometric Distributions: A summary of Random Variable facts & formulas

📰 Check out these articles:

• Statistics in Court: Incorrect Probabilities: An exploration of the misuse of probability rules in court cases [source: Significance Magazine] ✍️ Practice:

• Practice FRQ #1: Some basic probability calculations using a discrete random variable

• Practice FRQ #2: Test your knowledge of binomial scenarios and simulations

• Practice FRQ #3: A scenario involving a two-way table

If you have more time or want to dig deeper:

💎 Check out some online applets:

• Dice & The Law of Large Numbers: Play with this applet to get a sense of how probability works over the “long run”
• Coin Flips: A similar applet using coin flips

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📊 Unit 5: Sampling Distributions

Big takeaways:

Unit 5 provides the bridge from descriptive statistics (Units 1-4) to inferential statistics (Units 6-9). After reviewing the Normal Distribution and introducing the idea of using sample statistics (like p or x) to estimate population parameters, we explore the creation of sampling distributions.

We meet the conditions for inference: random samples, large samples (for categorical variables, we need at least 10 expected successes and failures; for quantitative variables, we need n to be at least 30), and independent observations (which turns into the “10% rule” for sampling without replacement: if the sample size n is less than 10% of the population size N, we can do calculations as if we sampled with replacement).

If these conditions are met, the sampling distribution we build will be approximately Normal and all of our formulas for calculating the mean and standard deviation of sampling distributions on the formula sheet will hold. We then build sampling distributions for sample proportions/sample means and the difference of sample proportions/sample means.

Definitely do this:

📚 Read these study guides:

• 5.0 Unit 5 Overview
• 5.1 Introducing Statistics: Why is My Sample Not Like Yours?
• 5.2 The Normal Distribution, Revisited
• 5.3 The Central Limit Theorem
• 5.4 Biased and Unbiased Point Estimates
• 5.5 Sampling Distributions for Sample Proportions
• 5.6 Sampling Distributions for Differences in Sample Proportions
• 5.7 Sampling Distributions for Sample Means
• 5.8 Sampling Distributions for Differences in Sample Means

🎥 Watch these videos from the Fiveable archives:

• Sampling Distributions for Proportions: an intro to vocabulary surrounding sampling distributions, and a simulation using a virtual “candy machine”
• Sampling Distributions for Means: an intro to the building of a sampling distribution for x-bar and a summary of the Central Limit Theorem

✍️ Practice:

• Unit 5 Practice FRQ: describe a sampling distribution and compute an associated probability
• Sampling Distributions for Proportions
• Sampling Distributions for Means

If you have more time or want to dig deeper:

💎 Check out some online applets:

• The "Candy Machine": Build a sampling distribution for p-hat.
• Sampling Distribution for x-bar: See the Central Limit Theorem in action! Definitely try to make a “custom” graph to give the population a unique shape.

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⚖️ Unit 6: Inference for Categorical Data (Proportions)

Big takeaways:

Unit 6 is where we meet Confidence Intervals and Hypothesis Tests for the first time, specifically z-intervals and z-tests for population proportions. After learning “the basics” about confidence intervals (what’s a confidence level? What’s a margin of error?), we construct and interpret 1 and 2-sample z-intervals.

These intervals, built from samples, can be used to justify claims about a population. Then, after exploring the rationale behind hypothesis tests (including how to write null/alternative hypotheses and interpret a p-value in context), we run 1 and 2-sample z-tests. Finally, we meet “Errors”: both Type I (rejecting a true H0) and Type II (failing to reject a false H0), and define the “Power” of a test as the probability of correctly rejecting a false H0. This unit is often heavily tested and is well worth your time to review!

Definitely do this:

📚 Read these study guides:

• 6.0 Unit 6 Overview
• 6.1 Introducing Statistics: Why Be Normal?
• 6.2 Constructing a Confidence Interval for a Population Proportion
• 6.3 Justifying a Claim Based on a Confidence Interval for a Population Proportion
• 6.4 Setting up a Test for a Population Proportion
• 6.5 Interpreting P-Values
• 6.6 Concluding a Test for a Population Proportion
• 6.7 Potential Errors When Performing Tests
• 6.8 Confidence Intervals for the Difference of Two Proportions
• 6.9 Justifying a Claim Based on a Confidence Interval for a Difference of Population Proportions
• 6.10 Setting Up a Test for the Difference of Two Population Proportions
• 6.11 Carrying Out a Test for the Difference of Two Population Proportions

🎥 Watch these videos from the Fiveable archives:

• Confidence intervals for p: An intro to Confidence Intervals and a breakdown of how to construct and interpret 1-sample z-intervals.
• Hypothesis Tests for p: An intro to Hypothesis Tests and practice running 1 and 2-sample z-tests.
• Errors & Power of a Test: A breakdown of the types of errors in hypothesis testing, and how to increase the power of a test.

📰 Check out these articles:

• Understanding Type I and Type II Errors: A breakdown of the Types of Errors with “boy who cried wolf” examples [Source: William Schmarzo] ✍️ Practice:

• Unit 6 Practice FRQ #1: Test your knowledge about Confidence Intervals!

If you have more time or want to dig deeper:

💎 Check out some online applets:

• Confidence Intervals for p: play with the population parameters and see what we mean by “confidence level”
• Reasoning of a Hypothesis Test: demonstrates the idea of Hypothesis Testing using basketball free-throws.

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😼 Unit 7: Inference for Quantitative Data (Means)

Big takeaways:

Unit 7 is an extension of Unit 6: we basically do everything again, but with t-procedures instead of z-procedures! We build Confidence Intervals and run Hypothesis Tests for a population mean or a difference of population means.

For the difference of population means, we must be able to distinguish between if we are running a 2-sample procedure or a matched-pairs procedure (in which we will use a 1-sample procedure to execute the process).

Definitely do this:

📚 Read these study guides:

• 7.0 Unit 7 Overview
• 7.1 Introducing Statistics: Should I Worry About Error?
• 7.2 Constructing a Confidence Interval for a Population Mean
• 7.3 Justifying a Claim About a Population Mean Based on a Confidence Interval
• 7.4 Setting Up a Test for a Population Mean
• 7.5 Carrying Out a Test for a Population Mean
• 7.6 Confidence Intervals for the Difference of Two Means
• 7.7 Justifying a Claim About the Difference of Two Means Based on a Confidence Interval
• 7.8 Setting up a Test for the Difference of Two Population Means
• 7.9 Carrying Out a Test for the Difference of Two Population Means
• 7.10 Skills Focus: Selecting, Implementing, and Communicating Inference Procedures

🎥 Watch these videos from the Fiveable archives:

• Hypothesis Tests for Mu: Lots of good FRQ practice
• Errors & Power of a Test: A breakdown of the types of errors in hypothesis testing, and how to increase the power of a test. (same as from Unit 6)
• Review of z and t procedures: A (mostly) comprehensive review of Units 6 and 7. Great for last-minute preparations!

✍️ Practice:

• Unit 7 Practice FRQ #1: Should we shut down the production line?
• Inference: Confidence Intervals for Means

If you have more time or want to dig deeper:

💎 Check out some online applets:

• Confidence Intervals for Mu: play with the population parameters and see what we mean by “confidence level”
• Statistical Power: Explore how the “Power” of a test is impacted by various inputs

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✳️ Unit 8: Inference for Categorical Data (Chi-Square)

Big takeaways:

Unit 8 is where we learn about chi-square tests, which can be used when there are two or more categorical variables. We’ll learn how to select from the following tests: the chi-square test for goodness of fit (for a distribution of proportions of one categorical variable in a population), the chi-square test for independence (for associations between categorical variables within a single population), or the chi-square test for homogeneity (for comparing distributions of a categorical variable across populations or treatments). 

Definitely do this:

📚 Read these study guides:

• 8.0 Unit 8 Overview
• 8.1 Introducing Statistics: Are My Results Unexpected?
• 8.2 Setting Up a Chi Square Goodness of Fit Test
• 8.3 Carrying out a Chi Square Goodness of Fit Test
• 8.4 Expected Counts in Two Way Tables
• 8.5 Setting up a Chi-Square Test for Homogeneity or Independence
• 8.6 Carrying Out a Chi-Square Test for Homogeneity or Independence
• 8.7 Skills Focus: Selecting an Appropriate Inference Procedure for Categorical Data

🎥 Watch these videos:

• Setting Up a Chi-Square Goodness of Fit Test
• Carrying Out a Chi-Square Goodness of Fit Test
• Carrying Out a Chi-Square Test for Homogeneity
• Carrying Out a Chi-Square Test for Independence

📰 Check out these articles:

• Chi-Square Test for Independence

✍️ Practice:

• 2013 FRQs
• 2014 FRQs
• 2016 FRQs
• 2017 FRQs

If you have more time or want to dig deeper:

💎 Check out some online applets:

• Chi-Square Distribution
• Using Chi-Square Statistic in Research

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📈 Unit 9: Inference for Quantitative Data (Slopes)

Big takeaways:

Unit 9 will teach students how to construct confidence intervals for and perform significance tests about the slope of a population regression line when appropriate conditions are met. Surprisingly, there is variability in slope, which differs from students’ experience in previous courses. Slopes will likely vary as part of an approximately normal sampling distribution centered at the (true) slope of the population regression line relating spring length to hanging mass. 

Definitely do this:

📚 Read these study guides:

• 9.0 Unit 9 Overview
• 9.1 Introducing Statistics: Do Those Points Align?
• 9.2 Confidence Intervals for the Slope of a Regression Model
• 9.3 Justifying a Claim About the Slope of a Regression Model Based on a Confidence Interval
• 9.4 Setting Up a Test for the Slope of a Regression Model
• 9.5 Carrying Out a Test for the Slope of a Regression Model
• 9.6 Skills Focus: Selecting an Appropriate Inference Procedure

🎥 Watch these videos:

• Introducing Statistics: Do the Points Align?
• Confidence Intervals for the Slope of a Regression Model
• Carrying Out a Test for the Slope of a Regression Model

✍️ Practice:

• 2007 Form B FRQ 6 part b
• Unit 8 & 9 (Chi-Square and Slopes) FRQs

If you have more time or want to dig deeper: 

💎 Check out some online applets:

• Practice MCQs