In statistical inference, or hypothesis testing, the traditional tests are called *parametric tests* because they depend on the specification of a probability distribution (such as the normal) except for a set of free parameters. Parametric tests are said to depend on distributional assumptions. *Nonparametric tests*, on the other hand, do not require any strict distributional assumptions. Even if the data are distributed normally, nonparametric methods are often almost as powerful as parametric methods.

Many nonparametric methods analyze the ranks of a variable rather than the original values. Procedures such as PROC NPAR1WAY calculate the ranks for you and then perform appropriate nonparametric tests. However, there are some situations in which you use a procedure such as PROC RANK to calculate ranks and then use another procedure to perform the appropriate test. See the section Obtaining Ranks for details.

Although the NPAR1WAY procedure is specifically targeted for nonparametric analysis, many other procedures also perform nonparametric analyses. Some general references on nonparametrics include Hollander and Wolfe (1999), Conover (1999), Gibbons and Chakraborti (1992), Hettmansperger (1984), Randles and Wolfe (1979), and Lehmann (1975).