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 High School
- Differences among various kinds of studies and inferences that can be drawn from them
- Characteristics of well-designed studies
- Measurement data and categorical data
- Univariate and bivariate data
- Histograms, parallel box plots, and scatter plots
- Understanding basic statistics
- Distinction between a statistic and a parameter
- Univariate and bivariate measurement data
- Bivariate data where at least one variable is categorical
- Linear transformations of univariate data and their affects
- Trends in bivariate data
- Simulations to explore the variability of sample statistics from a known population
- Sampling distributions
- Understand how sample statistics reflect the values of population parameters
- Sampling distributions as the basis for informal inference;
- Statistical techniques used to monitor process characteristics
- Sample space and probability distribution
- Simulations to construct empirical probability distributions
- Expected value of random variables
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Middle School
- Characteristics shared by two populations
- Different characteristics within one population
- Graphical representations of data
- Mean and interquartile range
- Data sets and their graphical representations
- Develop and evaluate inferences and predictions that are based on data
- Observation of populations using samples
- Relationships between two characteristics of a sample
- Complementary and mutually exclusive events
- Proportionality
- Basic understanding of probability
- Probabilities for simple compound events
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Elementary School
- Data-collection methods and effects
- Representation of data
- Differences in representing categorical and numerical data
- Features of a set of data and compare related data sets
- Distribution of data
- measures of center, focusing on the median
- Compare different representations of the same data
- Likely, unlikely, certain, equally likely and impossible events
- Probability of outcomes of simple experiments
- Likelihood of an event
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