Editing Nonparametric statistics (section)

==Applications and purpose==
Non-parametric methods are widely used for studying populations that have a ranked order (such as movie reviews receiving one to five "stars").  The use of non-parametric methods may be necessary when data have a [[ranking]] but no clear [[Number|numerical]] interpretation, such as when assessing [[preferences]]. In terms of [[level of measurement|levels of measurement]], non-parametric methods result in [[ordinal data]].

As non-parametric methods make fewer assumptions, their applicability is much more general than the corresponding parametric methods. In particular, they may be applied in situations where less is known about the application in question.  Also, due to the reliance on fewer assumptions, non-parametric methods are more [[Robust statistics#Introduction|robust]].

Non-parametric methods are sometimes considered simpler to use and more robust than parametric methods, even when the assumptions of parametric methods are justified. This is due to their more general nature, which may make them less susceptible to misuse and misunderstanding. Non-parametric methods can be considered a conservative choice, as they will work even when their assumptions are not met, whereas parametric methods can produce misleading results when their assumptions are violated.

The wider applicability and increased [[Robust statistics|robustness]] of non-parametric tests comes at a cost: in cases where a parametric test's assumptions are met, non-parametric tests have less [[statistical power]].  In other words, a larger sample size can be required to draw conclusions with the same degree of confidence.