title: Inference in ecology and evolution.
abstract: Most ecologists and evolutionary biologists continue to rely heavily on null hypothesis significance testing, rather than on recently advocated alternatives, for inference. Here, we briefly review null hypothesis significance testing and its major alternatives. We identify major objectives of statistical analysis and suggest which analytical approaches are appropriate for each. Any well designed study can improve our understanding of biological systems, regardless of the inferential approach used. Nevertheless, an awareness of available techniques and their pitfalls could guide better approaches to data collection and broaden the range of questions that can be addressed. Although we should reduce our reliance on significance testing, it retains an important role in statistical education and is likely to remain fundamental to the falsification of scientific hypotheses.
authors.affiliation: Department of Mathematics, University of Bristol, University Walk, Bristol, BS8 1TW, UK. philip.stephens@bristol.ac.uk
authors.forename: Philip A
authors.initials: PA
authors.lastname: Stephens
authors.forename: Steven W
authors.initials: SW
authors.lastname: Buskirk
authors.forename: Carlos Martínez
authors.initials: CM
authors.lastname: del Rio
mesh_terms: D001499:Bayes Theorem; D005075:Biological Evolution; D003627:Data Interpretation, Statistical; D004463:Ecology / Q000706:statistics & numerical data*; D015233:Models, Statistical