AP Statistics

Course Overview

Our AP Statistics course introduces students to the fundamental concepts and tools for collecting, analyzing, and drawing conclusions from data. Unlike traditional mathematics courses that focus on abstract concepts, AP Statistics emphasizes statistical reasoning and real-world applications that students will encounter in college and their careers.

This course covers four major themes: exploring data patterns and descriptions, sampling and experimentation planning, probability and simulation, and statistical inference. Students learn to think statistically about data collection, analysis techniques, and the interpretation of results in context. The course emphasizes conceptual understanding over computation, preparing students for data-driven decision making in various fields.

What Makes Our Course Special

What You'll Learn

• Master descriptive statistics and graphical representations of data
• Understand probability theory and apply it to real-world scenarios
• Learn proper experimental design and sampling techniques
• Develop skills in statistical inference, hypothesis testing, and confidence intervals
• Practice regression analysis and correlation interpretation
• Build expertise in communicating statistical findings effectively
• Apply statistical thinking to solve problems across various disciplines
• Master calculator and technology tools for statistical analysis

Detailed Course Curriculum

• Unit 1: Exploring One-Variable Data 12 hours

  Introduction to statistical thinking and methods for describing single-variable datasets.

  • Graphical displays: histograms, box plots, stem plots, and dot plots
  • Measures of center and spread: mean, median, mode, range, IQR, standard deviation
  • Describing distributions: shape, outliers, and unusual features
  • Standardizing data with z-scores and the empirical rule
  • Comparing distributions and making comparative statements

• Unit 2: Exploring Two-Variable Data 10 hours

  Analysis of relationships between two quantitative variables.

  • Scatterplots and correlation coefficient interpretation
  • Linear regression: least squares method and line of best fit
  • Residual analysis and assessing model fit
  • Transformations to achieve linearity
  • Categorical data analysis and two-way tables

• Unit 3: Collecting Data 8 hours

  Methods for planning studies and collecting data to answer statistical questions.

  • Sampling methods: simple random, stratified, cluster, and systematic
  • Experimental design principles: randomization, replication, and control
  • Observational studies vs. experiments: establishing causation
  • Sources of bias and methods to reduce bias
  • Ethics in data collection and analysis

• Unit 4: Probability, Random Variables, and Probability Distributions 15 hours

  Fundamental probability concepts and distributions commonly used in statistics.

  • Basic probability rules and conditional probability
  • Discrete and continuous random variables
  • Binomial, geometric, and normal probability distributions
  • Central Limit Theorem and sampling distributions
  • Simulation techniques for probability problems

• Unit 5: Sampling Distributions 8 hours

  Understanding how sample statistics vary and behave as estimators.

  • Sampling distribution of sample proportions
  • Sampling distribution of sample means
  • Central Limit Theorem applications
  • Standard error and its interpretation
  • Conditions for normal approximations

• Unit 6: Inference for Categorical Data: Proportions 8 hours

  Statistical inference methods for population proportions.

  • Confidence intervals for one and two proportions
  • Hypothesis testing for one and two proportions
  • Conditions for inference and assumption checking
  • Chi-square tests for goodness of fit and independence
  • P-value interpretation and statistical significance

• Unit 7: Inference for Quantitative Data: Means 12 hours

  Statistical inference methods for population means.

  • Confidence intervals for one and two means
  • One-sample and two-sample t-tests
  • Paired t-procedures for matched pairs data
  • Conditions for t-procedures and robustness
  • Power and Type I/Type II errors

• Unit 8: Inference for Categorical Data: Chi-Square 6 hours

  Advanced chi-square tests and applications.

  • Chi-square goodness of fit tests
  • Chi-square tests of independence/homogeneity
  • Expected counts and degrees of freedom
  • Conditions for chi-square tests
  • Follow-up analysis for significant results

• Unit 9: Inference for Quantitative Data: Slopes 6 hours

  Inference about the slope of a regression line.

  • Conditions for inference about slope
  • Confidence intervals and hypothesis tests for slope
  • Residual analysis and model assumptions
  • Prediction intervals vs. confidence intervals
  • Transformations and inference

Ready to Think Statistically?

AP Statistics is more than just a mathematics course – it's training in critical thinking and data literacy that will serve you throughout your academic and professional career. In our increasingly data-driven world, the ability to collect, analyze, and interpret data is invaluable across virtually every field.

Students who master statistical thinking develop a skeptical, analytical mindset that helps them evaluate claims, design research, and make informed decisions. Whether you're planning a career in business, science, social sciences, or any field that uses data, this course provides essential foundational skills.