Journal article

Publication year: 2007

Pages: 102-108

Journal: Acta Oecologica

Volume number: 31

Issue number: 1

DOI Link: https://doi.org/10.1016/j.actao.2006.10.002

URL: http://www.sciencedirect.com/science/article/pii/S1146609X06001196

Publisher: Elsevier

Abstract: 

Biological data often tend to have heterogeneous,
discontinuous non-normal distributions. Statistical non-parametric tests, like
the Mann–Whitney U-test or the extension for more
than two samples, the Kruskal–Wallis test, are often used in these cases, although
they assume certain preconditions which are often ignored. We developed a
permutation test procedure that uses the ratio of the interquartile distances
and the median differences of the original non-classified data to assess the
properties of the real distribution more appropriately than the classical
methods. We used this test on a heterogeneous, skewed biological data set on invertebrate
dispersal and showed how different the reactions of the Kruskal–Wallis test and
the permutation approach are. We then evaluated the new testing procedure with
reproducible data that were generated from the normal distribution. Here, we
tested the influence of four different experimental trials on the new testing
procedure in comparison to the Kruskal–Wallis test. These trials showed the
impact of data that were varying in terms of (a) negative correlation between
variances and means of the samples, (b) changing variances that were not
correlated with the means of the samples, (c) constant variances and means, but
different sample sizes and in trials (d) we evaluated the testing power of the
new procedure. Due to the different test statistics, the permutation test
reacted more sensibly to the data presented in trials (a) and c) and
non-uniformly in trial (b). In the evaluation of the testing power, no
significant differences between the Kruskal–Wallis test and the new permutation
testing procedure could be detected. We consider this test to be an alternative
for working on heterogeneous data where the preconditions of the classical
non-parametric tests are not met.

Harvard Citation style: Jopp, F. and Lange, C. (2007) Improving data interpretation of fragmentary data-sets on invertebrate dispersal with permutation tests, Acta Oecologica, 31(1), pp. 102-108. https://doi.org/10.1016/j.actao.2006.10.002

APA Citation style: Jopp, F., & Lange, C. (2007). Improving data interpretation of fragmentary data-sets on invertebrate dispersal with permutation tests. Acta Oecologica. 31(1), 102-108. https://doi.org/10.1016/j.actao.2006.10.002