Nonparametric Statistics for the Biological Sciences
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Nonparametric statistics provide a useful purpose for inferential analyses when data: (1) do not meet the purported precision of an interval scale, (2) there are serious concerns about extreme deviation from normal distribution, and (3) there is considerable difference in the number of subjects for each breakout group. It is not totally uncommon to hear terms such as ranking tests and distribution-free tests to describe the inferential tests associated with nonparametric statistics, due to the use of nominal and ordinal data and data that may not meet the desired assumption of normal distribution (i.e., bell-shaped curve). Although those who work in the biological sciences would ideally like to have precise measurement for their data, to have data that follow normal distribution patterns, and to have adequately-sized samples for all breakout groups, only too often these three desires are not met. Nonparametric statistics and the many inferential tests associated with nonparametric statistics provide a valuable set of options on how these data can be used to good effect. Following along with these aspirations, the R environment and the many external packages associated with R offer many practical applications that support inferential tests associated with nonparametric statistics.