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A protocol for data exploration to avoid common statistical problems
Zuur, A.F.; Ieno, E.N.; Elphick, C.S. (2010). A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1(1): 3-14. https://hdl.handle.net/10.1111/j.2041-210x.2009.00001.x
In: Methods in Ecology and Evolution. Wiley: Bognor Regis. ISSN 2041-2096; e-ISSN 2041-210X, more
Peer reviewed article  

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Keywords
    Collinearity
    Transformations
Author keywords
    data exploration; independence; type I and II errors; zero inflation

Authors  Top 
  • Zuur, A.F.
  • Ieno, E.N.
  • Elphick, C.S.

Abstract
    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.

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