Non-parametric methods of statistical analysis are a large collection of techniques that do not involve making prior assumptions about the distribution of the variables being investigated. They are often considered as less powerful than parametric methods, but are much more widely applicable since they involve few if any assumptions and are often more robust. Although non-parametric methods have been developed over a very long period, in recent years they have been augmented by visualization tools and a substantial number of new techniques that are essentially computationally driven.
Many of the more familiar non-parametric methods and tests are described in this Handbook, for example: goodness of fit tests, rank correlation methods, contingency table analysis, non-parametric ANOVA and non-parametric regression methods (e.g. the use of smoothing functions). In many cases data values are replaced by their ranks, i.e. the data are arranged in order and the observed values are replaced with their ordinal value or rank.