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Learn data analysis, probability, sampling, inference, and statistical reasoning for the AP Statistics exam.
This AP Statistics course on Study Mondo covers 41 topics organized across 9 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.
All content is completely free. Start with any category below, or jump to a specific topic that you need help with.
Take a diagnostic test covering all AP Statistics units. Find your weak areas and get a targeted study plan.
Pick the plan that matches your timeline โ from a 1-month build-up to a night-before review.
Jump into high-impact topics and keep your study momentum moving.
Variable types, distributions, summary statistics, and the normal model
Learn to identify categorical vs. quantitative data, and understand different sampling methods.
Create and interpret histograms, dotplots, stemplots, bar graphs, and pie charts.
Describe the shape, center, spread, and outliers of a distribution using SOCS.
Calculate and interpret mean, median, and mode as measures of central tendency.
A structured 4-week plan that builds mastery without burning out.
~60 hours total over 4 weeks
Calculate and interpret range, IQR, variance, and standard deviation.
Scatterplots, correlation, least-squares regression, residuals, and transformations
Create scatterplots and calculate the correlation coefficient r to describe linear relationships.
Find and interpret the least-squares regression line (LSRL) and make predictions.
Analyze residual plots to assess the fit of a regression model.
Interpret rยฒ as the proportion of variability explained by the regression model.
Use power, logarithmic, and exponential transformations to achieve linearity.
Sampling methods, observational studies vs. experiments, randomization, and bias
Compare simple random sampling, stratified, cluster, and systematic sampling methods.
Distinguish between observational studies and experiments, and understand causation vs. association.
Design experiments using control, randomization, replication, and blocking.
Identify sources of bias in sampling and surveys including voluntary response and convenience sampling.
Probability rules, conditional probability, random variables, binomial and geometric distributions
Apply addition and multiplication rules, and understand complements and mutually exclusive events.
Calculate conditional probabilities using formulas and two-way tables.
Test for independence using probability rules and understand its implications.
Define discrete random variables, calculate expected value, variance, and standard deviation.
Compute and interpret the expected value, variance, and standard deviation of a discrete random variable from its probability distribution.
Apply rules for the mean and variance of sums/differences of independent random variables, including linear transformations aX + b.
Understand continuous random variables, probability density functions, and uniform distributions.
Apply the binomial distribution to count successes in fixed trials with conditions BINS.
Use the geometric distribution to model the number of trials until the first success.
Sampling variability, the Central Limit Theorem, and sampling distributions of means and proportions
Understand sampling distributions and the variability of sample statistics.
Apply the Central Limit Theorem to approximate sampling distributions as Normal.
Properties of the sampling distribution of xฬ: mean ฮผ, standard error ฯ/โn, and shape via the Central Limit Theorem.
Properties of the sampling distribution of pฬ: mean p, standard error โ(p(1-p)/n), Large Counts condition, and normal approximation.
Confidence intervals and significance tests for one and two proportions
Construct and interpret confidence intervals for a population proportion.
Perform one-sample and two-sample z-tests for proportions.
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
Set up hypothesis tests with null and alternative hypotheses, significance level, and p-values.
Understand Type I and Type II errors, their probabilities, and the concept of power.
Construct and interpret confidence intervals for a population mean using the t-distribution.
Correctly interpret confidence intervals and understand confidence level meaning.
Perform one-sample and two-sample t-tests for means.
Analyze paired data using the paired t-test and matched pairs designs.
Two-sample t-interval and t-test for the difference in two population means ฮผ1 - ฮผ2 using independent samples.
Chi-square tests for goodness-of-fit, independence, and homogeneity
Perform chi-square tests for goodness of fit, homogeneity, and independence.
Chi-square test for independence (one sample, two categorical variables) and for homogeneity (multiple independent samples), including expected counts, degrees of freedom, and conditions.
Inference for the slope of a least-squares regression line