AP Statistics was revised for the 2026-27 school year and is now organized into 5 CED units (down from 9), which CollegeBoard weights differently on the exam. The largest unit is worth 20-30% of the multiple-choice section and the smallest 10-20%, so check the weightings before you plan revision rather than splitting your time equally.
This guide covers all 5 units, their weight, and where to spend your time first. If you want to practice against the exact exam format, Cognito's AP Statistics past papers library has released papers with full written FRQ responses.
The 5 units at a glance
| Unit | Topic | Exam weighting |
|---|---|---|
| 1 | Exploring One-Variable Data and Collecting Data | 20-30% |
| 2 | Probability, Random Variables, and Probability Distributions | 15-25% |
| 3 | Inference for Categorical Data: Proportions | 15-25% |
| 4 | Inference for Quantitative Data: Means | 10-20% |
| 5 | Regression Analysis | 10-20% |
Units 1, 2 and 3 together account for at least half of the multiple-choice section. Units 4 and 5 are smaller by weighting, but inference for means and regression both show up regularly on FRQs, so they're worth prep time.
Unit 1: Exploring One-Variable Data and Collecting Data (20-30%)
Unit 1 is the heaviest unit. It covers descriptive statistics (mean, median, standard deviation, IQR), graph types (histograms, boxplots, dotplots), z-scores and normal distributions, and describing distributions. It also covers sampling methods (simple random, stratified, cluster, systematic, convenience), experimental design (randomization, control, blocking, replication, blinding), observational studies vs experiments, and sources of bias.
What to prioritize: Describing distributions in context, designing an experiment with random assignment (this appears on the FRQ frequently), distinguishing observational studies from experiments and knowing what conclusions each allows (association vs causation), and identifying specific types of bias (undercoverage, nonresponse, response bias, voluntary response).
Unit 2: Probability, Random Variables, and Probability Distributions (15-25%)
Unit 2 is foundational for the inference units. It covers probability rules (addition, multiplication, conditional probability, independence), discrete random variables, the binomial distribution, and continuous random variables.
What to prioritize: Conditional probability (P(A given B), and when to use it) and the binomial distribution (conditions, mean and SD formulas). The geometric distribution and combining random variables are no longer part of the course.
Unit 3: Inference for Categorical Data: Proportions (15-25%)
Unit 3 starts with the sampling distribution of a sample proportion, then covers confidence intervals and significance tests for one and two proportions, and chi-square tests for independence and homogeneity. Every inference procedure follows the same four-part structure: State (hypotheses), Plan (conditions), Do (calculate), Conclude (in context).
What to prioritize: The four-part structure, checking all three conditions (random, 10%, large counts), distinguishing a test for independence (one sample, two categorical variables) from a test for homogeneity (several samples or groups), and writing conclusions in context that reference the specific study.
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Unit 4: Inference for Quantitative Data: Means (10-20%)
Unit 4 starts with the sampling distribution of a sample mean and the Central Limit Theorem, then covers confidence intervals and significance tests for one mean, two means (independent samples), and matched pairs, using t-distributions.
What to prioritize: What the Central Limit Theorem really says (and doesn't), recognizing when a scenario calls for matched pairs vs two independent samples (a common wrong-procedure error on FRQs), and checking the normality condition (either sample size >= 30 or approximate normality of the underlying population).
Unit 5: Regression Analysis (10-20%)
Unit 5 covers scatterplots, the correlation coefficient (r), least-squares regression lines, residual plots, the coefficient of determination (r-squared), and interpreting slope and intercept in context. Inference for slope is no longer part of the course.
What to prioritize: Interpreting slope and intercept in the context of the specific variables (this is where FRQ points are lost), reading a residual plot to check whether a linear model is appropriate, and understanding that correlation doesn't imply causation.
Where to focus your revision
For a 10-week revision plan, a defensible weighting is roughly 2 weeks on Unit 1, 1.5 weeks on Unit 2, 2 weeks on Unit 3 (including chi-square tests), 1.5 weeks on Unit 4 (with heavy overlap in procedure structure with Unit 3), and 1 week on Unit 5. The final 2 weeks should be full timed past papers with written FRQ responses, marked against the official scoring guidelines.
AP Stats FRQs reward precise language in the state and conclude steps far more than most students expect. Working through 20+ released FRQs with full written responses is the fastest way to internalize the rubric. Clear, in-context wording in the conclusion is part of what earns full credit on each free-response question.
Every inference FRQ follows the same four-part structure: State, Plan, Do, Conclude. Missing any part loses points, even if the math is correct. Build a personal template for each procedure and drill it until it's automatic.
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Full library of released past papers with worked solutions and full written FRQ responses that show the level of context the rubric rewards.
