AP Statistics

In this unit, students will analyze and interpret one-variable data using graphical and numerical methods and evaluate appropriate methods for collecting data through sampling and experimental design.

Students will:

• Identify and classify components of a statistical study, including population, sample, variables, and investigative questions.

• Represent and construct graphical and tabular displays for categorical and quantitative data.

• Calculate and interpret numerical summaries of quantitative data, including center, spread, position, and outliers.

• Describe and compare distributions using shape, variability, and standardized values (z-scores).

• Calculate and interpret z-scores and relative position within distributions.

• Identify and classify sampling methods and sources of bias in data collection.

• Differentiate and analyze observational studies and experiments, including study design components.

• Evaluate and justify conclusions based on data collection methods and study design.

In this unit, students will apply probability rules and models to quantify uncertainty and interpret probability distributions of random variables in context.

Students will:

• Identify and classify events, sample spaces, and probability models.

• Calculate and determine probabilities using probability rules, including conditional probability and independence.

• Represent and construct probability models using tables, diagrams, and simulations.

• Describe and analyze relationships between events using probability concepts.

• Represent and interpret probability distributions for discrete and continuous random variables.

• Calculate and interpret expected value and standard deviation of random variables.

• Describe and analyze normal and binomial distributions in context.

• Evaluate and justify probability models and simulation-based reasoning.

In this unit, students will use sampling distributions to construct confidence intervals and conduct hypothesis tests to make statistical inferences about population proportions.

Students will:

• Identify and classify conditions for inference about population proportions and categorical variables.

• Represent and construct sampling distributions for sample proportions and differences in proportions as the foundation for inference.

• Calculate and interpret confidence intervals for population proportions and differences in proportions.

• Calculate and determine hypothesis tests for population proportions and differences in proportions using p-values and significance levels.

• Describe and analyze sampling variability in proportions and categorical data in context.

• Interpret and evaluate p-values, statistical significance, and conditions for valid inference.

• Describe and analyze Type I and Type II errors, power, and decision-making in statistical inference.

• Perform and interpret chi-square tests for independence and homogeneity using categorical data.

In this unit, students will apply confidence intervals and hypothesis tests to draw statistical conclusions about population means and differences of means.

Students will:

• Identify and classify conditions for inference about population means and differences in means.

• Represent and describe sampling distributions for sample means.

• Calculate and interpret confidence intervals for population means.

• Calculate and determine confidence intervals for differences in means.

• Calculate and determine hypothesis tests for population means.

• Calculate and determine hypothesis tests for differences in means.

• Describe and analyze statistical results in context using inference procedures.

• Evaluate and justify assumptions, robustness, and validity of inference methods.

In this unit, students will analyze relationships between two quantitative variables using regression and correlation models and evaluate the appropriateness of predictions and conclusions.

Students will:

• Identify and classify relationships between two quantitative variables.

• Represent and construct linear regression models for bivariate data.

• Calculate and interpret slope, intercept, and correlation in context.

• Calculate and interpret residuals to assess model fit.

• Describe and analyze patterns in residual plots.

• Use and interpret regression models for prediction.

• Evaluate and analyze limitations and appropriateness of regression models.

• Evaluate and justify conclusions about association and prediction using statistical evidence.