Abstract
The Mann–Whitney U test is often viewed as the nonparametric equivalent of Student’s t-Test for Independent Samples, but this comparison may be somewhat too convenient. The two tests (the nonparametric Mann–Whitney U-Test and the parametric Student’s t-Test for Independent Samples) may have similar purposes in that they are both used to determine if there are statistically significant differences between two groups. However, the Mann–Whitney U-Test is used with nonparametric data (typically, ordinal data) whereas the Student’s t-Test for Independent Samples is used with data that meet the assumptions associated with parametric distributions (typically interval data that approximate an acceptable level of normal distribution). Even so, the Mann–Whitney U-Test has many appropriate uses and it should be considered when using ranked data, data that deviate from acceptable distribution patterns, or for when there are noticeable differences in the number of subjects in the two comparative groups.
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Notes
- 1.
Review the factor() function and the as.factor() function to see appropriate applications of each, specifically for when object variables are recoded from one class to another class.
- 2.
Each format has specific advantages. All figures associated with this text were saved in.PNG (i.e., Portable Network Graphics) format, because.PNG figures are effective and their inclusion eliminates any concerns about free use.
- 3.
Separate from the use of R or any other software, be sure to study the many possibilities for how data can be graphically displayed. Start with simple tools, such as the histogram and boxplot. Then, with more practice and understanding, move to more sophisticated tools such as density plot, violin plot, etc. An Internet search on graphical display of data or some similar term will provide an ample number of quality resources on this topic.
- 4.
In the R syntax shown immediately below, notice how the with() function was used for selection. In association with this approach, note the use of two equal signs (i.e., ==) and not one equal sign.
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MacFarland, T.W., Yates, J.M. (2016). Mann–Whitney U Test . In: Introduction to Nonparametric Statistics for the Biological Sciences Using R. Springer, Cham. https://doi.org/10.1007/978-3-319-30634-6_4
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DOI: https://doi.org/10.1007/978-3-319-30634-6_4
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