Interactive study labs for the revised (2026–27) AP Statistics course, one for each of its 55 topics. Each lab has data you can edit, graphs and simulations that react, a running explanation of what changed and why, worked examples, FRQ tips, practice questions, and the key terms for that topic.
Every key term in the course, with search, flashcards, and a self-quiz.
Tell statistical questions from ordinary ones, then sample from a population to see why statistics vary while parameters stay fixed.
Open lab → Topic 1.2Classify variables as categorical or quantitative (discrete or continuous) and see which summaries and graphs fit each type.
Open lab → Topic 1.3Build frequency and relative frequency tables for a categorical variable and work backward from percents to counts.
Open lab → Topic 1.4Display a categorical variable with bar charts and pie charts, and catch a misleading graph with a truncated axis.
Open lab → Topic 1.5Graph one quantitative variable as a dotplot, stemplot, histogram, or cumulative relative frequency plot, and see how bin choices change the picture.
Open lab → Topic 1.6Describe a quantitative distribution's shape, center, variability, and unusual features, and practice naming shapes from a histogram.
Open lab → Topic 1.7Calculate the mean, median, standard deviation, quartiles, and IQR step by step, and see which ones resist outliers.
Open lab → Topic 1.8Build a boxplot from the five-number summary, flag outliers with the fences, and read percentiles off a cumulative relative frequency graph.
Open lab → Topic 1.9Compare two groups on shape, outliers, center, and spread with side-by-side dotplots and boxplots, and practice writing comparisons in context.
Open lab → Topic 1.10Tell statistical questions from non-statistical ones, classify studies as census, sample survey, observational study, or experiment, and see how a confounding variable fakes a cause.
Open lab → Topic 1.11Sample a simulated school with simple random, stratified, cluster, and systematic sampling, and see which method gives the most precise estimates.
Open lab → Topic 1.12Run a town survey six different ways and watch how undercoverage, nonresponse, voluntary response, convenience sampling, and question wording push the estimate away from the truth.
Open lab → Topic 1.13Run simulated experiments with self-selection, random assignment, blocking, and matched pairs to see how design controls bias and variability, then decide the scope of inference.
Open lab →Build a two-way table, then compare groups with side-by-side, segmented, and mosaic bar charts to see whether two categorical variables are associated.
Open lab → Topic 2.2Click any cell of a two-way table to see its joint, marginal, and conditional relative frequencies, then practice picking the right denominator.
Open lab → Topic 2.3Design and run a simulation with random numbers, then watch the estimated probability settle toward the true value as the number of trials grows.
Open lab → Topic 2.4List a sample space, count the outcomes in an event, and use the complement rule; then check whether a probability model is valid.
Open lab → Topic 2.5Pick two events from a deck of cards, see their overlap in a Venn diagram, and decide whether they are mutually exclusive.
Open lab → Topic 2.6Use a tree diagram and a table of hypothetical counts to find P(A and B), P(B), and P(A | B), and see why P(A | B) and P(B | A) can be very different.
Open lab → Topic 2.7Set P(A), P(B), and P(A and B), then check independence and find P(A or B) with the general addition rule.
Open lab → Topic 2.8Build a discrete probability distribution, check that it is valid, and find probabilities like P(X ≤ 2) from the table and graph.
Open lab → Topic 2.9Find the mean (expected value) and standard deviation of a discrete random variable, then see how they change when you transform or combine variables.
Open lab → Topic 2.10Check the binomial conditions, then set n and p to find exact, at-most, and at-least probabilities with binompdf and binomcdf.
Open lab → Topic 2.11Shade areas under a normal curve, convert to z-scores, and go both ways with normalcdf and invNorm.
Open lab → Topic 2.12Draw thousands of random samples from skewed, uniform, or yes/no populations and watch the sampling distribution of the mean become approximately normal.
Open lab →Take hundreds of samples and see what makes an estimator biased or variable, and why bigger samples fix one but not the other.
Open lab → Topic 3.2Simulate thousands of samples to see the shape, center, and spread of p̂, and when the normal model and its conditions work.
Open lab → Topic 3.3Build a one-proportion z-interval step by step, simulate hundreds of intervals to see what confidence means, and plan a sample size.
Open lab → Topic 3.4Use a confidence interval as a set of plausible values to decide whether a claim about a population proportion is supported.
Open lab → Topic 3.5Practice writing null and alternative hypotheses, defining the parameter, and checking conditions for a one-proportion z-test.
Open lab → Topic 3.6See a p-value as a shaded area under the null model, compare it with a simulation, and practice interpreting it in context.
Open lab → Topic 3.7Run a complete one-sample z-test for a proportion and get a four-step write-up (State, Plan, Do, Conclude) built from your inputs.
Open lab → Topic 3.8See Type I error, Type II error, and power as areas under two sampling distributions, and how n, α, and the true p change them.
Open lab → Topic 3.9Simulate thousands of pairs of samples to see the center, spread, and shape of the sampling distribution of p̂₁ − p̂₂.
Open lab → Topic 3.10Build a two-sample z-interval for p₁ − p₂ step by step, then simulate many intervals to see what the confidence level means.
Open lab → Topic 3.11Decide whether a confidence interval for p₁ − p₂ supports, fails to support, or contradicts a claim, and see what controls its width.
Open lab → Topic 3.12Write hypotheses for p₁ − p₂, pick the right alternative, and check conditions using the pooled proportion.
Open lab → Topic 3.13Run a complete two-sample z-test for p₁ − p₂: pooled SE, test statistic, p-value, decision, and conclusion in context.
Open lab → Topic 3.14Decide between a chi-square test for homogeneity and for independence, write hypotheses, compute expected counts, and check conditions.
Open lab → Topic 3.15Compute expected counts, components, the χ² statistic, df, and p-value for a two-way table, then write the conclusion in context.
Open lab →Draw thousands of random samples from different population shapes and watch the distribution of sample means center at μ, shrink by σ/√n, and turn normal as n grows.
Open lab → Topic 4.2Build a one-sample or paired t interval from raw data or summary statistics, check the conditions, and see what the confidence level really means.
Open lab → Topic 4.3Use a t interval for a mean or mean difference to decide whether a claimed value is plausible, and see how sample size and confidence level change the answer.
Open lab → Topic 4.4Practice choosing the parameter, hypotheses, procedure, and conditions for a one-sample or paired t test, and see which t values would count as evidence for the alternative.
Open lab → Topic 4.5Run a one-sample or paired t test from raw data or summary statistics: compute t and the p-value, see the area on the t curve, and write all four steps.
Open lab → Topic 4.6Simulate thousands of differences in sample means and see why the mean is μ₁ − μ₂, the variances add, and the shape becomes normal.
Open lab → Topic 4.7Build a two-sample t interval step by step, compare calculator and conservative df, and watch 50 intervals show what confidence means.
Open lab → Topic 4.8Decide whether a claim about μ₁ − μ₂ is supported, plausible, or contradicted by a confidence interval, and see what makes the interval narrower.
Open lab → Topic 4.9Tell paired data from two independent samples, write hypotheses about μ₁ − μ₂, and check the Random, 10%, and Normal/Large conditions.
Open lab → Topic 4.10Run a complete two-sample t-test: test statistic, df, a shaded p-value, the decision, and a four-step written answer that updates live.
Open lab →Drag and add points on a scatterplot, then practice describing direction, form, strength, and unusual features in context.
Open lab → Topic 5.2Guess the correlation from a scatterplot, compute r step by step from z-scores, and practice telling correlation from causation.
Open lab → Topic 5.3Use a regression equation to make predictions, interpret the slope and y-intercept in context, and spot risky extrapolation.
Open lab → Topic 5.4Calculate residuals as actual minus predicted, interpret them in context, and read residual plots to judge whether a linear model fits.
Open lab → Topic 5.5Drag points and watch the least-squares line, its squared residuals, slope, intercept, r², and s update, then read regression output.
Open lab →