Use of an Improved Statistical Method for Group Comparisons to Study Effects of Prairie Fire

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Abstract
This paper describes an improved method for performing statistical comparisons among experimental groups. This technique, termed multi—response permutation procedures (MRPP), is similar in purpose to the t test and one—way analysis of variance F test. However, in contrast to these, the new method features very relaxed requirements on the data structure, is easily applied to multivariate problems, and makes it possible to relate the analysis visually to the perceived data space. The MRPP test statistic is based on the within—group average of pairwise distance measures between object response values in a euclidian data space. The null distribution of the test statistic is based on the collection of all possible permutations of the objects into groups having specified sizes. For large group sizes, this distribution is approximated by a continuous distribution satisfying three exact moments. The advantages and applications of MRPP are illustrated using both artificial examples and empirical data on total August standing crop in mixed prairie following an October wildfire. The MRPP analyses of the empirical data revealed that there were no differences in standing crop between burned an unburned areas after the first postfire growing seasons, standing crop was significantly greater in the previously burned areas.

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