High School Statistics & Probability Common Core Standards
43 standards across 4 domains.
Interpreting Categorical & Quantitative Data
Summarizing, representing and interpreting data on one or two variables, including linear models.
HSS-ID.1 Represent data with plots on the real number line (dot plots, histograms, and box plots).
HSS-ID.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.
HSS-ID.3 Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers).
HSS-ID.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.
HSS-ID.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.
HSS-ID.6 Represent data on two quantitative variables on a scatter plot, and describe how the variables are related.
HSS-ID.6a Fit a function to the data; use functions fitted to data to solve problems in the context of the data.
HSS-ID.6b Informally assess the fit of a function by plotting and analyzing residuals.
HSS-ID.6c Fit a linear function for a scatter plot that suggests a linear association.
HSS-ID.7 Interpret the slope (rate of change) and the intercept (constant term) of a linear model in the context of the data.
HSS-ID.8 Compute (using technology) and interpret the correlation coefficient of a linear fit.
HSS-ID.9 Distinguish between correlation and causation.
HSS-ID.A Summarize, represent, and interpret data on a single count or measurement variable
HSS-ID.B Summarize, represent, and interpret data on two categorical and quantitative variables
HSS-ID.C Interpret linear models
Making Inferences & Justifying Conclusions
Understanding statistical processes and drawing conclusions from samples, experiments and observational studies.
HSS-IC.1 Understand statistics as a process for making inferences about population parameters based on a random sample from that population.
HSS-IC.2 Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation.
HSS-IC.3 Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each.
HSS-IC.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.
HSS-IC.5 Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant.
HSS-IC.6 Evaluate reports based on data.
HSS-IC.A Understand and evaluate random processes underlying statistical experiments
HSS-IC.B Make inferences and justify conclusions from sample surveys, experiments, and observational studies
Conditional Probability & the Rules of Probability
Understanding independence and conditional probability and using them to interpret data.
HSS-CP.1 Describe events as subsets of a sample space (the set of outcomes) using characteristics (or categories) of the outcomes, or as unions, intersections, or complements of other events ("or," "and," "not").
HSS-CP.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.
HSS-CP.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.
HSS-CP.4 Construct and interpret two-way frequency tables of data when two categories are associated with each object being classified. Use the two-way table as a sample space to decide if events are independent and to approximate conditional probabilities.
HSS-CP.5 Recognize and explain the concepts of conditional probability and independence in everyday language and everyday situations.
HSS-CP.6 Find the conditional probability of A given B as the fraction of B's outcomes that also belong to A, and interpret the answer in terms of the model.
HSS-CP.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.
HSS-CP.8 (+) Apply the general Multiplication Rule in a uniform probability model, P(A and B) = P(A)P(B|A) = P(B)P(A|B), and interpret the answer in terms of the model.
HSS-CP.9 (+) Use permutations and combinations to compute probabilities of compound events and solve problems.
HSS-CP.A Understand independence and conditional probability and use them to interpret data
HSS-CP.B Use the rules of probability to compute probabilities of compound events in a uniform probability model
Using Probability to Make Decisions
Calculating expected values and using probability to evaluate the outcomes of decisions.
HSS-MD.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.
HSS-MD.2 (+) Calculate the expected value of a random variable; interpret it as the mean of the probability distribution.
HSS-MD.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.
HSS-MD.4 (+) Develop a probability distribution for a random variable defined for a sample space in which probabilities are assigned empirically; find the expected value.
HSS-MD.5 (+) Weigh the possible outcomes of a decision by assigning probabilities to payoff values and finding expected values.
HSS-MD.5a Find the expected payoff for a game of chance.
HSS-MD.5b Evaluate and compare strategies on the basis of expected values.
HSS-MD.6 (+) Use probabilities to make fair decisions (e.g., drawing by lots, using a random number generator).
HSS-MD.7 (+) Analyze decisions and strategies using probability concepts (e.g., product testing, medical testing, pulling a hockey goalie at the end of a game).
HSS-MD.A Calculate expected values and use them to solve problems
HSS-MD.B Use probability to evaluate outcomes of decisions
How many High School Statistics & Probability Common Core Standards are there?
There are 43 High School Statistics & Probability Common Core Standards on this page, in 4 domains: Interpreting Categorical & Quantitative Data, Making Inferences & Justifying Conclusions, Conditional Probability & the Rules of Probability and Using Probability to Make Decisions.
How do I write an assessment for a High School Statistics & Probability Common Core Math standard?
Pick a standard on this page and choose Make a quiz or Make an exit ticket. AssessmentWiz opens the generator with the standard's code and text filled in, so every question targets that standard.
What does a code like HSS-ID.1 mean?
High school Common Core math codes read category-domain.standard. In HSS-ID.1, "HS" plus the letter names the conceptual category (A is Algebra, F is Functions, G is Geometry, N is Number & Quantity, S is Statistics & Probability), the letters after the hyphen name the domain, and the number is the standard. A trailing letter marks one part of a standard.