WisconsinWisconsin High School Statistics Curriculum
Video lessons and practice for every Statistics topic. Aligned to Wisconsin Standards for Math. Get help with data, probability, and inference anytime.
Wisconsin High School Statistics Curriculum | StudyPugHelp
ID | Standard | StudyPug Topic |
|---|---|---|
CC.HSS.ID.A.1 | Represent data with plots on the real number line (dot plots, histograms, and box plots). |
CC.HSS.ID.A.2 | Use statistics appropriate to the shape of the data distribution to compare center (median, mean) and spread (interquartile range, standard deviation) of two or more different data sets. |
CC.HSS.ID.A.3 | Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers). |
CC.HSS.ID.A.4 | Use the mean and standard deviation of a data set to fit it to a normal distribution and to estimate population percentages. Recognize that there are data sets for which such a procedure is not appropriate. Use calculators, spreadsheets, and tables to estimate areas under the normal curve. |
CC.HSS.ID.B.5 | Summarize categorical data for two categories in two-way frequency tables. Interpret relative frequencies in the context of the data (including joint, marginal, and conditional relative frequencies). Recognize possible associations and trends in the data. |
CC.HSS.ID.B.6 | Represent data on two quantitative variables on a scatter plot, and describe how the variables are related. |
CC.HSS.IC.A.1 | Understand statistics as a process for making inferences about population parameters based on a random sample from that population. |
CC.HSS.IC.A.2 | Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. |
CC.HSS.IC.B.3 | Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each. |
CC.HSS.IC.B.4 | Use data from a sample survey to estimate a population mean or proportion; develop a margin of error through the use of simulation models for random sampling. |
CC.HSS.IC.B.5 | Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant. |
CC.HSS.IC.B.6 | Evaluate reports based on data. |
CC.HSS.CP.A.2 | Understand that two events A and B are independent if the probability of A and B occurring together is the product of their probabilities, and use this characterization to determine if they are independent. |
CC.HSS.CP.A.3 | Understand the conditional probability of A given B as P(A and B)/P(B), and interpret independence of A and B as saying that the conditional probability of A given B is the same as the probability of A, and the conditional probability of B given A is the same as the probability of B. |
CC.HSS.CP.A.5 | Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations. |
CC.HSS.CP.B.7 | Apply the Addition Rule, P(A or B) = P(A) + P(B) - P(A and B), and interpret the answer in terms of the model. |
CC.HSS.CP.B.9 | Use permutations and combinations to compute probabilities of compound events and solve problems. |
CC.HSS.MD.A.1 | Define a random variable for a quantity of interest by assigning a numerical value to each event in a sample space; graph the corresponding probability distribution using the same graphical displays as for data distributions. |
CC.HSS.MD.A.2 | Calculate the expected value of a random variable; interpret it as the mean of the probability distribution. |
CC.HSS.MD.A.3 | Develop a probability distribution for a random variable defined for a sample space in which theoretical probabilities can be calculated; find the expected value. |
CC.HSS.MD.B.7 | Analyze decisions and strategies using probability concepts (e.g., product testing, medical testing, pulling a hockey goalie at the end of a game). |
Wisconsin High School Statistics: What Students Learn
High school Statistics in Wisconsin gives students the tools to collect, analyze, and interpret data. Aligned to the Wisconsin Standards for Math, this course covers everything from reading dot plots and histograms to understanding complex probability models and making inferences from sample data.
Data Analysis and Distributions
Students begin by representing data using dot plots, histograms, and box plots. They compare data sets using measures of center like mean and median, and measures of spread like interquartile range and standard deviation. A key skill is recognizing the effect of outliers on shape, center, and spread.
- Represent data on dot plots, histograms, and box plots
- Compare center and spread across two or more data sets
- Fit data to a normal distribution and estimate population percentages
- Summarize categorical data in two-way frequency tables
Scatter Plots, Correlation, and Causation
Students learn to represent two-variable data on scatter plots and use technology to compute the correlation coefficient of a linear fit. A critical distinction taught at this level is the difference between correlation and causation — a concept with real-world applications in science, medicine, and public policy.
Statistical Inference and Sampling
This section covers how statistics is used to make inferences about populations from random samples. Students explore sample surveys, experiments, and observational studies, and learn how randomization applies to each. They use simulation models to develop margins of error and evaluate the significance of results from randomized experiments.
- Estimate population means and proportions from sample data
- Develop margin of error using simulation models
- Compare two treatments using randomized experiment data
- Evaluate reports and claims based on data
Probability Rules and Conditional Probability
Students build a strong foundation in probability, including independent events, conditional probability, and the Addition and Multiplication Rules. Two-way frequency tables serve as sample spaces for exploring independence and approximating conditional probabilities in context.
- Identify independent events using P(A and B) = P(A) × P(B)
- Calculate conditional probability using P(A|B) = P(A and B)/P(B)
- Apply the Addition Rule: P(A or B) = P(A) + P(B) − P(A and B)
- Apply the general Multiplication Rule in uniform probability models
- Use permutations and combinations to find probabilities of compound events
Random Variables and Expected Value
The final major topic area focuses on random variables and probability distributions. Students define random variables, graph probability distributions, and calculate expected value. They develop distributions from both theoretical probabilities and empirical data, then use expected value to evaluate decisions, design fair games, and analyze real-world strategies.
- Define and graph probability distributions for random variables
- Calculate and interpret expected value as the mean of a distribution
- Weigh payoff values using probability to support decision-making
- Analyze decisions using probability — from product testing to medical screening
How StudyPug Supports Wisconsin Statistics Students
StudyPug provides video lessons and practice problems for every topic in the Wisconsin high school Statistics curriculum. Each lesson is short, focused, and easy to replay — ideal for reviewing before a test or catching up after a missed class. All content follows the Wisconsin Standards for Math, so students and parents can trust that what they're studying matches what their school teaches.