A frequent goal of collecting data is to draw inferences about a population from a sample. In such cases, inferential statistics provide the bases on which to draw such conclusions that go beyond the observed data. An example of a common inference is evaluating the likelihood that an observed effect (e.g., difference in group means) is not attributable to chance. Unlike descriptive statistics that simply summarize observed data, inferential statistics are used to make more general statements about the world beyond the data.

In order to derive the desired inferences, one can rely on any number of models that are often broadly characterized as either parametric or nonparametric. Common examples of analytic methods that are associated with parametric models (e.g., general linear model) include analysis of ...

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