Journal cover Journal topic
Web Ecology An open-access peer-reviewed journal
Journal topic
Volume 8, issue 1
Web Ecol., 8, 67–73, 2008
https://doi.org/10.5194/we-8-67-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.
Web Ecol., 8, 67–73, 2008
https://doi.org/10.5194/we-8-67-2008
© Author(s) 2008. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 Jun 2008

06 Jun 2008

Unexpected failures of recommended tests in basic statistical analyses of ecological data

Z. Kikvidze1 and J. Moya-Laraño2 Z. Kikvidze and J. Moya-Laraño
  • 1Room 568, Environmental Bldg, The Univ. of Tokyo, 5-1-5 Kashiwanoha, 2778663 Kashiwa, Japan
  • 2Room 303, Estacion Experimental de Zonas Aridas, CSIC, General Segura 1, 04001 Almeria, Spain

Abstract. Ecologists, when analyzing the output of simple experiments, often have to compare statistical samples that simultaneously are of uneven size, unequal variance and distribute non-normally. Although there are special tests designed to address each of these unsuitable characteristics, it is unclear how their combination affects the tests. Here we compare the performance of recommended tests using generated data sets that simulate statistical samples typical in ecological research. We measured rates of type I and II errors, and found that common parametric tests such as ANOVA are quite robust to non-normality, uneven sample size, unequal variance, and their effect combined. ANOVA and randomization tests produced very similar results. At the same time, the t-test for unequal variance unexpectedly lost power with samples of uneven size. Also, non-parametric tests were strongly affected by unequal variance in large samples, yet non-parametric tests could complement parametric tests when testing samples of uneven size. Thus, we demonstrate that the robustness of each kind of test strongly depends on the combination of parameters (distribution, sample size, equality of variances). We conclude that manuals should be revised to offer more elaborate instructions for applying specific statistical tests.

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