AP Statistics Unit Tests
Pick a unit to drill it head-on. Each unit has 4 different test variations so you can keep retaking until you master it.
Unit 1–2: Exploring Data
One- and two-variable data, distributions, summary stats, and graphical displays.
12 questions · ~18 min
Unit 3: Sampling & Experimentation
Sampling methods, experiments vs observational studies, and bias.
12 questions · ~18 min
Unit 4–5: Probability, Random Variables & Distributions
Conditional probability, independence, random variables, and the binomial / normal distributions.
12 questions · ~18 min
Unit 6–7: Confidence Intervals
Constructing and interpreting confidence intervals for proportions and means.
12 questions · ~18 min
Unit 6–7: Hypothesis Testing
Significance tests for proportions, means, and chi-square. Type I/II errors and power.
12 questions · ~18 min
Unit 9: Regression & Inference for Slopes
Linear regression, residuals, correlation, and inference for the slope.
12 questions · ~18 min
How unit tests work
- Focused on a single AP unit, between a single-topic quiz and the full diagnostic.
- 4 variations per unit — pick a fresh variation any time you retake.
- Roughly 90 seconds per question — the same pacing as the AP exam.
- You'll get a unit-level score, recommended topics to review, and a question-by-question explanation.
About the AP Statistics exam
AP Statistics is equivalent to a one-semester, introductory, non-calculus-based college statistics course. The curriculum is organized around four major themes: exploring and describing data, designing studies and collecting data (sampling and experiments), anticipating patterns through probability and probability distributions, and statistical inference through confidence intervals and significance tests. Unlike most math courses, AP Statistics rewards clear written communication far more than computation. Students are expected to interpret results in context, state and check the conditions for inference procedures, and explain what a confidence interval or p-value actually means in plain language. The exam's free-response section is where this matters most: vague or templated answers lose credit, and graders look for responses that connect statistical reasoning to the specific scenario. The course also emphasizes the logic of experimental design—randomization, control, replication, and the crucial distinction between observational studies (which cannot establish causation) and experiments (which can). Common difficulties include confusing the conditions for different inference procedures, misinterpreting probability, and writing conclusions that fail to address the original question. Because much of the computation can be handled by a graphing calculator, success depends on choosing the correct procedure, verifying assumptions, and communicating conclusions precisely. The capstone Investigative Task asks students to extend familiar ideas to an unfamiliar situation, testing genuine statistical thinking rather than memorized formulas. Strong preparation pairs vocabulary precision—sampling distribution versus population distribution, Type I versus Type II error—with extensive practice writing full inference responses that name the procedure, check conditions, compute, and interpret in context.
Exam structure
Two equally weighted sections totaling 3 hours: Section I is 40 multiple-choice questions in 1 hour 30 minutes (50%), and Section II is 6 free-response questions in 1 hour 30 minutes (50%) consisting of 5 standard questions and one longer Investigative Task. A graphing calculator is allowed throughout.
Scoring
Multiple-choice points and rubric-scored free-response points (standard FRQs and the higher-value Investigative Task) are combined into a weighted composite converted to a 1-5 AP score, with 3 considered passing.
Common mistakes
- Failing to check and state conditions/assumptions before running an inference procedure
- Interpreting a p-value or confidence level incorrectly (e.g., claiming 95% probability the parameter is in one specific interval)
- Confusing observational studies with experiments and claiming causation from observational data
- Writing conclusions that are not stated in the context of the problem
- Mixing up sampling distribution, population distribution, and distribution of a sample