AP Statistics
Learn data analysis, probability, sampling, inference, and statistical reasoning for the AP Statistics exam.
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Take the free AP Statistics diagnostic
It finds what you already know and builds your study plan: the topics to clear first, each a short lesson plus an exit quiz.
Take the free diagnosticCourse Overview
This AP Statistics course on Study Mondo covers 37 topics organized across 8 categories. Each topic includes detailed written explanations, worked examples, practice problems with step-by-step solutions, flashcards for review, and interactive lessons to help you master the material.
What You'll Learn
Unit 1: Exploring One-Variable Data
Variable types, distributions, summary statistics, and the normal model
Unit 2: Exploring Two-Variable Data
Scatterplots, correlation, least-squares regression, residuals, and transformations
Unit 3: Collecting Data
Sampling methods, observational studies vs. experiments, randomization, and bias
Unit 4: Probability, Random Variables, and Probability Distributions
Probability rules, conditional probability, random variables, binomial and geometric distributions
Unit 5: Sampling Distributions
Sampling variability, the Central Limit Theorem, and sampling distributions of means and proportions
Unit 6: Inference for Categorical Data — Proportions
Confidence intervals and significance tests for one and two proportions
Unit 7: Inference for Quantitative Data — Means
Confidence intervals and significance tests for one mean, paired means, and two means
Unit 8: Inference for Categorical Data — Chi-Square
Chi-square tests for goodness-of-fit, independence, and homogeneity
Start with any category below, or jump to a specific topic that you need help with.
Study tools
📚 Study schedules & cram guides
Pick the schedule that matches your timeline — from a 1-month build-up to a night-before review.
A focused 72-hour rescue plan when the exam is almost here.
~12 hours total study
One full week to lock in the highest-leverage topics and FRQ patterns.
~25 hours total study
A structured 4-week plan that builds mastery without burning out.
~60 hours total over 4 weeks
How to attack free-response questions and earn easy partial credit.
~3-4 hours of focused work
The night-before checklist: top formulas, common traps, and what NOT to do.
~45 minutes to skim
Explore Related Topics
Jump into high-impact topics and keep your study momentum moving.
Variable types, distributions, summary statistics, and the normal model
Types of Data and Sampling
Learn to identify categorical vs. quantitative data, and understand different sampling methods.
Displaying Distributions with Graphs
Create and interpret histograms, dotplots, stemplots, bar graphs, and pie charts.
Describing Distributions
Describe the shape, center, spread, and outliers of a distribution using SOCS.
Measures of Center
Calculate and interpret mean, median, and mode as measures of central tendency.
Measures of Spread
Calculate and interpret range, IQR, variance, and standard deviation.
Normal Distributions
Use the Normal distribution, z-scores, and the empirical rule to find probabilities.
Scatterplots, correlation, least-squares regression, residuals, and transformations
Scatter Plots and Correlation
Create scatterplots and calculate the correlation coefficient r to describe linear relationships.
Least-Squares Regression
Find and interpret the least-squares regression line (LSRL) and make predictions.
Residuals and Residual Plots
Analyze residual plots to assess the fit of a regression model.
Coefficient of Determination
Interpret r² as the proportion of variability explained by the regression model.
Sampling methods, observational studies vs. experiments, randomization, and bias
Sampling Methods
Compare simple random sampling, stratified, cluster, and systematic sampling methods.
Observational Studies vs Experiments
Distinguish between observational studies and experiments, and understand causation vs. association.
Experimental Design
Design experiments using control, randomization, replication, and blocking.
Bias in Sampling and Surveys
Identify sources of bias in sampling and surveys including voluntary response and convenience sampling.
Probability rules, conditional probability, random variables, binomial and geometric distributions
Basic Probability Rules
Apply addition and multiplication rules, and understand complements and mutually exclusive events.
Conditional Probability
Calculate conditional probabilities using formulas and two-way tables.
Independence
Test for independence using probability rules and understand its implications.
Discrete Random Variables
Define discrete random variables, calculate expected value, variance, and standard deviation.
Mean and Standard Deviation of a Discrete Random Variable
Compute and interpret the expected value, variance, and standard deviation of a discrete random variable from its probability distribution.
Continuous Random Variables
Understand continuous random variables, probability density functions, and uniform distributions.
Binomial Distribution
Apply the binomial distribution to count successes in fixed trials with conditions BINS.
Sampling variability, the Central Limit Theorem, and sampling distributions of means and proportions
Sampling Distributions
Understand sampling distributions and the variability of sample statistics.
Central Limit Theorem
Apply the Central Limit Theorem to approximate sampling distributions as Normal.
Sampling Distribution of the Sample Mean
Properties of the sampling distribution of x̄: mean μ, standard error σ/√n, and shape via the Central Limit Theorem.
Sampling Distribution of the Sample Proportion
Properties of the sampling distribution of p̂: mean p, standard error √(p(1-p)/n), Large Counts condition, and normal approximation.
Confidence intervals and significance tests for one and two proportions
Confidence Intervals for Proportions
Construct and interpret confidence intervals for a population proportion.
Tests for Proportions
Perform one-sample and two-sample z-tests for proportions.
Inference for Two Sample Proportions (CI and Test)
Two-sample z-interval and z-test for the difference in two population proportions p1 - p2, including conditions and pooled vs unpooled SE.
Confidence intervals and significance tests for one mean, paired means, and two means
Hypothesis Testing Framework
Set up hypothesis tests with null and alternative hypotheses, significance level, and p-values.
Type I and Type II Errors
Understand Type I and Type II errors, their probabilities, and the concept of power.
Confidence Intervals for Means
Construct and interpret confidence intervals for a population mean using the t-distribution.
Interpreting Confidence Intervals
Correctly interpret confidence intervals and understand confidence level meaning.
Tests for Means
Perform one-sample and two-sample t-tests for means.
Paired Data
Analyze paired data using the paired t-test and matched pairs designs.
Inference for Two Sample Means (CI and Test)
Two-sample t-interval and t-test for the difference in two population means μ1 - μ2 using independent samples.
Chi-square tests for goodness-of-fit, independence, and homogeneity
Chi-Square Tests
Perform chi-square tests for goodness of fit, homogeneity, and independence.
Chi-Square Tests for Independence and Homogeneity
Chi-square test for independence (one sample, two categorical variables) and for homogeneity (multiple independent samples), including expected counts, degrees of freedom, and conditions.