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A protocol for data exploration to avoid common statistical problems
Article first published online: 13 NOV 2009
© 2009 The Authors. Journal compilation © 2009 British Ecological Society
Methods in Ecology and Evolution
Volume 1, Issue 1, pages 3–14, March 2010
How to Cite
Zuur, A. F., Ieno, E. N. and Elphick, C. S. (2010), A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution, 1: 3–14. doi: 10.1111/j.2041-210X.2009.00001.x
- Issue published online: 23 FEB 2010
- Article first published online: 13 NOV 2009
- Received 13 August 2009; accepted 8 October 2009 Handling Editor: Robert P. Frecklenton
- data exploration;
- type I and II errors;
- zero inflation
1. While teaching statistics to ecologists, the lead authors of this paper have noticed common statistical problems. If a random sample of their work (including scientific papers) produced before doing these courses were selected, half would probably contain violations of the underlying assumptions of the statistical techniques employed.
2. Some violations have little impact on the results or ecological conclusions; yet others increase type I or type II errors, potentially resulting in wrong ecological conclusions. Most of these violations can be avoided by applying better data exploration. These problems are especially troublesome in applied ecology, where management and policy decisions are often at stake.
3. Here, we provide a protocol for data exploration; discuss current tools to detect outliers, heterogeneity of variance, collinearity, dependence of observations, problems with interactions, double zeros in multivariate analysis, zero inflation in generalized linear modelling, and the correct type of relationships between dependent and independent variables; and provide advice on how to address these problems when they arise. We also address misconceptions about normality, and provide advice on data transformations.
4. Data exploration avoids type I and type II errors, among other problems, thereby reducing the chance of making wrong ecological conclusions and poor recommendations. It is therefore essential for good quality management and policy based on statistical analyses.