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About the Course
Learn about the major concepts and tools used for collecting, analyzing, and drawing conclusions from data. You’ll explore statistics through discussion and activities, and you'll design surveys and experiments.
Skills You'll Learn
Selecting methods for collecting or analyzing data
Describing patterns, trends, associations, and relationships in data
Using probability and simulation to describe probability distributions and define uncertainty in statistical inference
Using statistical reasoning to draw appropriate conclusions and justify claims
Equivalency and Prerequisites
College Course Equivalent
A one-semester, introductory, non-calculus-based college course in statistics
Recommended Prerequisites
A second-year course in algebra
Exam Date
About the Units
The course content outlined below is organized into commonly taught units of study that provide one possible sequence for the course. Your teacher may choose to organize the course content differently based on local priorities and preferences.
Course Content
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
On The Exam
15%–23% of Score
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
On The Exam
5%–7% of Score
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
On The Exam
12%–15% of Score
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
On The Exam
10%–20% of Score
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
On The Exam
7%–12% of Score
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
On The Exam
12%–15% of Score
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
On The Exam
10%–18% of Score
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
On The Exam
2%–5% of Score
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
On The Exam
2%–5% of Score
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