Concept Taxonomy
This document defines the categorical taxonomy for organizing the 300 AP Statistics concepts.
Taxonomy Categories
| TaxonomyID | Category Name | Description |
|---|---|---|
| FOUND | Foundations | Core statistical terminology, data types, and fundamental concepts that underpin all other learning |
| EDA1 | Univariate Analysis | Concepts related to exploring, summarizing, and visualizing single-variable data |
| EDA2 | Bivariate Analysis | Concepts for exploring relationships between two variables including scatterplots and correlation |
| REG | Regression | Linear regression concepts including least squares, residuals, and model interpretation |
| STUDY | Study Design | Concepts for designing studies, experiments, sampling methods, and understanding bias |
| PROB | Probability | Probability rules, events, conditional probability, and probability models |
| RAND | Random Variables | Discrete random variables, expected value, and named distributions (binomial, geometric) |
| SAMP | Sampling Distributions | Sampling variability, sampling distributions, and the Central Limit Theorem |
| CIPR | CI for Proportions | Confidence intervals for one and two proportions |
| HTPR | HT for Proportions | Hypothesis testing concepts and tests for proportions |
| TMEA | T-Procedures for Means | T-distribution, confidence intervals, and hypothesis tests for means |
| CHISQ | Chi-Square Tests | Chi-square distribution and tests for categorical data |
| REGF | Regression Inference | Inference for regression slopes and model conditions |
| COMM | Communication | Statistical report writing, interpreting results, and exam strategies |
Category Descriptions
FOUND - Foundations
Core building blocks including: statistics, data, variables (categorical, quantitative, discrete, continuous), population, sample, parameter, statistic. These concepts have few or no dependencies.
EDA1 - Univariate Analysis
Exploratory data analysis for single variables: distributions, graphical displays (histograms, boxplots, stemplots), measures of center (mean, median, mode), measures of spread (range, IQR, standard deviation), shape descriptions, outliers, normal distributions, z-scores.
EDA2 - Bivariate Analysis
Two-variable relationships: two-way tables, marginal and conditional distributions, scatterplots, direction/form/strength of association, correlation coefficient.
REG - Regression
Linear modeling: least squares regression, regression equation, slope and intercept interpretation, residuals, residual plots, coefficient of determination (R²), influential points, extrapolation dangers.
STUDY - Study Design
Data collection methods: observational studies vs experiments, treatments, random assignment, experimental designs (completely randomized, randomized block, matched pairs), blinding, confounding, sampling methods (SRS, stratified, cluster, systematic), bias types.
PROB - Probability
Probability foundations: sample space, events, probability rules (addition, multiplication), conditional probability, independence, tree diagrams, Venn diagrams, simulation, Law of Large Numbers.
RAND - Random Variables
Random variable theory: discrete random variables, probability distributions, expected value, variance, combining random variables, binomial distribution, geometric distribution.
SAMP - Sampling Distributions
Sampling theory: sampling variability, sampling distributions of proportions and means, unbiased estimators, Central Limit Theorem, normal approximation.
CIPR - CI for Proportions
Inference for proportions (confidence intervals): point estimates, margin of error, confidence level, critical values, standard error, conditions, interpretation, sample size determination.
HTPR - HT for Proportions
Hypothesis testing fundamentals: null and alternative hypotheses, test statistics, p-values, significance level, Type I and II errors, power, tests for one and two proportions.
TMEA - T-Procedures for Means
T-distribution based inference: degrees of freedom, one-sample and two-sample t-intervals, one-sample and two-sample t-tests, paired t-procedures, robustness.
CHISQ - Chi-Square Tests
Categorical data inference: chi-square distribution, chi-square statistic, expected and observed counts, goodness-of-fit test, test for homogeneity, test for independence.
REGF - Regression Inference
Inference for regression: t-interval and t-test for slope, standard error of slope, conditions for regression inference (LINE), interpreting regression output.
COMM - Communication
Statistical communication: practical vs statistical significance, effect size, study limitations, generalizability, report writing, four-step inference process, AP exam strategies.