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This section provides various rank-based hypothesis tests and descriptive functions which do not assume that your data are from normal distributions.
Rank-based methods
assume that your data have an underlying continuous distribution.
assume that for groups being compared, their parent distributions are similar in all characteristics other than location.
are usually less sensitive than parametric methods.
are often more robust than parametric methods when their assumptions are properly met.
are preferred less by some statisticians and more by others in comparison with the use of parametric methods on transformed data.
can run into problems when there are many ties (data with the same value).
that take into account the magnitude of the difference between categories (e.g. Wilcoxon signed ranks test) are more powerful than those that do not (e.g. sign test).
The numerical methods used in rank-based calculations have progressed in recent years. StatsDirect utilises modern developments, including some calculations of exact probability in the presence of tied data. An excellent account of nonparametric methods is given by Conover (1999).
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