Abstract
1. While teaching statistics to ecologists, the lead authors of this
paper have noticed common statistical
problems. If a randomsample 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.
Users
Please
log in to take part in the discussion (add own reviews or comments).