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Nonparametric Statistical Methods

Nonparametric statistical methods or distribution-free tests are statistical procedures that do not rely on assumptions about the underlying distribution of the data. These methods are useful when the assumptions of parametric tests are not met or when little is known about the population parameter of the variable of interest in the population. They are particularly useful when working with small sample sizes. This workshop will describe the principles of non-parametric methods, introduce the most commonly used methods, and, through examples, describe when the use of these methods is appropriate and contrast them with their parametric counterparts.

Data for Workshop:

Scripts for Workshop:

Fee: None to members of the Cornell community, but registration is required. Since space is limited, early registration is encouraged.

For times and locations of upcoming workshops, please see the Workshop Schedule.