AP Statistics Diagnostic

Short topic-level assessments built around documented student misconceptions. Each diagnostic is ten items keyed to a specific wrong way of thinking; miss one and the result routes you straight to the drill that fixes it. This page covers all five units and every one of the 55 topics of the redesigned 2026 course, whose first exam is May 2027. Pick a topic to start.

Unit 1

Exploring One-Variable Data and Collecting Data

1.1 Introducing Statistics: What Can We Learn from Data? Start → 1.2 Variables Start → 1.3 Tabular Representation and Summary Statistics for One Categorical Variable Start → 1.4 Graphical Representations for One Categorical Variable Start → 1.5 Graphical Representations for One Quantitative Variable Start → 1.6 Descriptions for One Quantitative Variable Distributions Start → 1.7 Summary Statistics for One Quantitative Variable Start → 1.8 Graphical Representations of Summary Statistics for One Quantitative Variable Start → 1.9 Comparisons of the Distributions for One Quantitative Variable Start → 1.10 The Investigative Question Revisited and Data Collection Start → 1.11 Random Sampling Start → 1.12 Potential Problems with Sampling Start → 1.13 Experimental Design Start →

Unit 2

Probability, Random Variables, and Probability Distributions

2.1 Tabular and Graphical Representations for the Distributions of Two Categorical Variables Start → 2.2 Summary Statistics for Two Categorical Variables Start → 2.3 Estimating Probabilities Using Simulation Start → 2.4 Introduction to Probability Start → 2.5 Mutually Exclusive Events Start → 2.6 Conditional Probability Start → 2.7 Independent Events and Unions of Events Start → 2.8 Introduction to Random Variables and Probability Distributions Start → 2.9 Parameters of Random Variables Start → 2.10 The Binomial Distribution Start → 2.11 The Normal Distribution Start → 2.12 Sampling Distributions and the Central Limit Theorem Start →

Unit 3

Inference for Categorical Data: Proportions

3.1 Estimators Start → 3.2 Sampling Distributions for Sample Proportions Start → 3.3 Constructing a Confidence Interval for a Population Proportion Start → 3.4 Justifying a Claim Based on a Confidence Interval for a Population Proportion Start → 3.5 Setting Up a Test for a Population Proportion Start → 3.6 p-Values Start → 3.7 Carrying Out a Test for a Population Proportion Start → 3.8 Potential Errors When Performing Tests Start → 3.9 Sampling Distributions for the Difference Between Sample Proportions Start → 3.10 Constructing a Confidence Interval for the Difference Between Two Population Proportions Start → 3.11 Justifying a Claim Based on a Confidence Interval for the Difference Between Two Population Proportions Start → 3.12 Setting Up a Test for the Difference Between Two Population Proportions Start → 3.13 Carrying Out a Test for the Difference Between Two Population Proportions Start → 3.14 Setting Up a Chi-Square Test for Homogeneity or Independence Start → 3.15 Carrying Out a Chi-Square Test for Homogeneity or Independence Start →

Unit 4

Inference for Quantitative Data: Means

4.1 Sampling Distributions for Sample Means Start → 4.2 Constructing a Confidence Interval for a Population Mean or Population Mean Difference Start → 4.3 Justifying a Claim Based on a Confidence Interval for a Population Mean or Population Mean Difference Start → 4.4 Setting Up a Test for a Population Mean or Population Mean Difference Start → 4.5 Carrying Out a Test for a Population Mean or Population Mean Difference Start → 4.6 Sampling Distributions for the Difference Between Two Sample Means Start → 4.7 Constructing a Confidence Interval for the Difference Between Two Population Means Start → 4.8 Justifying a Claim Based on a Confidence Interval for the Difference Between Two Population Means Start → 4.9 Setting Up a Test for the Difference Between Two Population Means Start → 4.10 Carrying Out a Test for the Difference Between Two Population Means Start →

Unit 5

Regression Analysis

5.1 Graphical Representations Between Two Quantitative Variables Start → 5.2 Correlation Start → 5.3 Linear Regression Models Start → 5.4 Residuals Start → 5.5 Least-Squares Regression Start →

All five units are live. Each diagnostic is about 10 questions and takes 8 to 12 minutes. Missed items map to named misconceptions; the result page links you straight to drills that target those specific failures.