Class Description AP Statistics Exam Prep

AP Statistics Exam Prep

2026 Spring

Class Description
This class will prepare students for the upcoming AP Statistics exam in May. The AP Statistics Exam will test your understanding of the mathematical concepts covered in the course units, including your ability to use statistical methods and calculate the probability of an outcome. You’ll need to bring a graphing calculator with statistics capabilities to the exam.

This is a hybrid digital exam. You’ll complete multiple-choice questions and view free-response questions in the Bluebook testing app. You’ll handwrite your free-response answers in paper exam booklets.

Homework
Weekly homework will be assigned. It will take approximately 45 – 90 minutes  to complete.

Prerequisite
Students should be currently taking AP Statistics in school.

Exam Date
Thursday, May 7, 2026 | 12 PM Local

Mr. Oksuz

Mr. Oksuz holds both a Bachelor’s and a Master’s Degree in Mathematics and brings over 12 years of teaching experience focused on high school math classes such as Algebra 1, Algebra 2, Pre-Calculus, AP Calculus, and AP Statistics. Throughout his teaching career, he also coached competitive math teams for Mathcounts, AMC 8, and the AMC 10/12.

In addition, Mr. Oksuz serves as an adjunct professor at a community college, where he continues to share his love for mathematics with students. Whether through mentoring students, contributing to math-related initiatives, or exploring real-world applications of mathematical principles, he is committed to fostering a love of learning that extends well beyond traditional teaching hours.

Exam Format

Unit 1: Exploring One-Variable Data
You’ll be introduced to how statisticians approach variation and practice representing data, describing distributions of data, and drawing conclusions based on a theoretical distribution.

Topics may include:

• Variation in categorical and quantitative variables
• Representing data using tables or graphs
• Calculating and interpreting statistics
• Describing and comparing distributions of data
• The normal distribution

Unit 2: Exploring Two-Variable Data
You’ll build on what you’ve learned by representing two-variable data, comparing distributions, describing relationships between variables, and using models to make predictions.

Topics may include:

• Comparing representations of 2 categorical variables
• Calculating statistics for 2 categorical variables
• Representing bivariate quantitative data using scatter plots
• Describing associations in bivariate data and interpreting correlation
• Linear regression models
• Residuals and residual plots
• Departures from linearity

Unit 3: Collecting Data
You’ll be introduced to study design, including the importance of randomization. You’ll understand how to interpret the results of well-designed studies to draw appropriate conclusions and generalizations.

Topics may include:

• Planning a study
• Sampling methods
• Sources of bias in sampling methods
• Designing an experiment
• Interpreting the results of an experiment

Unit 4: Probability, Random Variables, and Probability Distributions
You’ll learn the fundamentals of probability and be introduced to the probability distributions that are the basis for statistical inference.

Topics may include:

• Using simulation to estimate probabilities
• Calculating the probability of a random event
• Random variables and probability distributions
• The binomial distribution
• The geometric distribution

Unit 5: Sampling Distributions
As you build understanding of sampling distributions, you’ll lay the foundation for estimating characteristics of a population and quantifying confidence.

Topics may include:

• Variation in statistics for samples collected from the same population
• The central limit theorem
• Biased and unbiased point estimates
• Sampling distributions for sample proportions
• Sampling distributions for sample means

Unit 6: Inference for Categorical Data – Proportions
You’ll learn inference procedures for proportions of a categorical variable, building a foundation of understanding of statistical inference, a concept you’ll continue to explore throughout the course.

Topics may include:

• Constructing and interpreting a confidence interval for a population proportion
• Setting up and carrying out a test for a population proportion
• Interpreting a p-value and justifying a claim about a population proportion
• Type I and Type II errors in significance testing
• Confidence intervals and tests for the difference of 2 proportions

Unit 7: Inference for Quantitative Data – Means
Building on lessons learned about inference in Unit 6, you’ll learn to analyze quantitative data to make inferences about population means.

Topics may include:

• Constructing and interpreting a confidence interval for a population mean
• Setting up and carrying out a test for a population mean
• Interpreting a p-value and justifying a claim about a population mean
• Confidence intervals and tests for the difference of 2 population means

Unit 8: Inference for Categorical Data – Chi-Square
You’ll learn about chi-square tests, which can be used when there are two or more categorical variables.

Topics may include:

• The chi-square test for goodness of fit
• The chi-square test for homogeneity
• The chi-square test for independence
• Selecting an appropriate inference procedure for categorical data

Unit 9: Inference for Quantitative Data – Slopes
You’ll understand that the slope of a regression model is not necessarily the true slope but is based on a single sample from a sampling distribution, and you’ll learn how to construct confidence intervals and perform significance tests for this slope.

Topics may include:

• Confidence intervals for the slope of a regression model
• Setting up and carrying out a test for the slope of a regression model
• Selecting an appropriate inference procedure