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- 1Exploring One-Variable Data
- 2Exploring Two-Variable Data
- 3Collecting Data
- 4Probability, Random Variables, and Probability Distributions
- 5Sampling Distributions
- 6Inference for Categorical Data: Proportions
- 7Inference for Quantitative Data: Means
- 8Inference for Categorical Data: Chi-Square
- 9Inference for Quantitative Data: Slopes
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2026 1 files
- 2026 Questions
- AP Statistics — Question index
- 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
- 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
- 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
- 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
- 5.1 Introducing Statistics: Why Is My Sample Not Like Yours?
- 5.4 Biased and Unbiased Point Estimates
- 5.7 Sampling Distributions for Sample Means
- 5.8 Sampling Distributions for Differences in Sample Means
- 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
- 6.6 Concluding a Test for a Population Proportion
- 6.7 Potential Errors When Performing Tests
- 6.8 Confidence Intervals for the Difference of Two Proportions
- 6.10 Setting Up a Test for the Difference of Two Population Proportions
- 6.11 Carrying Out a Test for the Difference of Two Population Proportions
- 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
- 7.8 Setting Up a Test for the Difference of Two Population Means
- 7.9 Carrying Out a Test for the Difference of Two Population Means
- 8.5 Setting Up a Chi-Square Test for Homogeneity or Independence
- 8.6 Carrying Out a Chi-Square Test for Homogeneity or Independence
- 9.2 Confidence Intervals for the Slope of a Regression Model
- 9.5 Carrying Out a Test for the Slope of a Regression Model
- 1. Exploring One-Variable Data
- 2. Exploring Two-Variable Data
- 3. Collecting Data
- 4. Probability, Random Variables, and Probability Distributions
- 5. Sampling Distributions
- 6. Inference for Categorical Data: Proportions
- 7. Inference for Quantitative Data: Means
- 8. Inference for Categorical Data: Chi-Square
- 9. Inference for Quantitative Data: Slopes
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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 Test for Goodness of Fit
- 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
- 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
- 1. Exploring One-Variable Data
- 2. Exploring Two-Variable Data
- 3. Collecting Data
- 4. Probability, Random Variables, and Probability Distributions
- 5. Sampling Distributions
- 6. Inference for Categorical Data: Proportions
- 7. Inference for Quantitative Data: Means
- 8. Inference for Categorical Data: Chi-Square
- 9. Inference for Quantitative Data: Slopes
- 1. Exploring One-Variable Data
- 2. Exploring Two-Variable Data
- 3. Collecting Data
- 4. Probability, Random Variables, and Probability Distributions
- 5. Sampling Distributions
- 6. Inference for Categorical Data: Proportions
- 7. Inference for Quantitative Data: Means
- 8. Inference for Categorical Data: Chi-Square
- 9. Inference for Quantitative Data: Slopes
- Complete pack — exercise sheets + past papers
- 1 Exploring One-Variable Data — Part 1
- 1 Exploring One-Variable Data — Part 2
- 2 Exploring Two-Variable Data — Part 1
- 2 Exploring Two-Variable Data — Part 2
- 3 Collecting Data — Part 1
- 3 Collecting Data — Part 2
- 4 Probability, Random Variables, and Probability Distributions — Part 1
- 4 Probability, Random Variables, and Probability Distributions — Part 2
- 5 Sampling Distributions — Part 1
- 5 Sampling Distributions — Part 2
- 6 Inference for Categorical Data: Proportions — Part 1
- 6 Inference for Categorical Data: Proportions — Part 2
- 7 Inference for Quantitative Data: Means — Part 1
- 7 Inference for Quantitative Data: Means — Part 2
- 8 Inference for Categorical Data: Chi-Square — Part 1
- 8 Inference for Categorical Data: Chi-Square — Part 2
- 9 Inference for Quantitative Data: Slopes — Part 1
- 9 Inference for Quantitative Data: Slopes — Part 2
How to use these papers
Mark these against the scoring guidelines with the rubric structure in front of you, because this exam is scored on structure more than any other AP. Check each inference answer for all five stages: hypotheses, procedure named, conditions checked, computation, conclusion in context.
The conditions are the most commonly skipped stage and the cheapest to fix. Work through released questions doing nothing but the condition checks — no computation at all — until stating them is automatic.
Read the guidelines' commentary on what earned a partial rather than a full score. In this subject the difference is almost always a conclusion written as proof rather than as evidence against the null.