Search the California Content Standards

Cluster:
Understand and evaluate random processes underlying statistical experiments.

Standard:
Decide if a specified model is consistent with results from a given data-generating process, e.g., using simulation. For example, a model says a spinning coin falls heads up with probability 0.5. Would a result of 5 tails in a row cause you to question the model? *

Cluster:
Make inferences and justify conclusions from sample surveys, experiments, and observational studies.

Standard:
Recognize the purposes of and differences among sample surveys, experiments, and observational studies; explain how randomization relates to each. *

Cluster:
Make inferences and justify conclusions from sample surveys, experiments, and observational studies.

Standard:
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. *

Cluster:
Make inferences and justify conclusions from sample surveys, experiments, and observational studies.

Standard:
Use data from a randomized experiment to compare two treatments; use simulations to decide if differences between parameters are significant. *

Cluster:
Make inferences and justify conclusions from sample surveys, experiments, and observational studies.

Standard:
Evaluate reports based on data. *

Cluster:
Summarize, represent, and interpret data on a single count or measurement variable.

Standard:
Represent data with plots on the real number line (dot plots, histograms, and box plots). *

Cluster:
Summarize, represent, and interpret data on a single count or measurement variable.

Standard:
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. *

Cluster:
Summarize, represent, and interpret data on a single count or measurement variable.

Standard:
Interpret differences in shape, center, and spread in the context of the data sets, accounting for possible effects of extreme data points (outliers). *

Cluster:
Summarize, represent, and interpret data on a single count or measurement variable.

Standard:
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. *

Cluster:
Summarize, represent, and interpret data on two categorical and quantitative variables.

Standard:
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. *