Nonparametric methods. Clinical applications

In clinical research, "samples" are studied in order to get ideas about the characteristics of the larger populations from which the samples are taken. Population characteristics are called parameters (the population mean and standard deviation are examples). Parametric statistical methods are those that require estimates of parameters and assumptions about the source populations. Familiar examples of parametric methods are the t test, analysis of variance, and Pearson's correlation coefficient. Nonparametric (NP) methods do not require estimates of population parameters. These methods are sometimes called "distribution-free" because the samples of interest can be evaluated without concern for the shape (distribution) of the values in the populations providing the samples. NP methods also are called "ranking" or "ordering" tests, because the relative size or order of the observations may be evaluated, rather than requiring actual measurements. More than 30% of the research reports that appeared between July 1982 and June 1983, in four pediatric journals, employed at least one nonparametric method. The commonly used tests were chi-square, the Fisher exact test, and various "ranking" methods. An alphabetical list of common nonparametric tests is presented, with brief comments about each. Tables are presented, arranged by types of observations, so that the nature of the data guides the user to the method that might be used.