Statistical model specification and power: recommendations on the use of test-qualified pooling in analysis of experimental data.
A common approach to the analysis of experimental data across much of the biological sciences is test-qualified pooling. Here non-significant terms are dropped from a statistical model, effectively pooling the variation associated with each removed term with the error term used to test hypotheses (or estimate effect sizes). This pooling is only carried out if statistical testing on the basis of applying that data to a previous more complicated model provides motivation for this model simplification; hence the pooling is test-qualified. In pooling, the researcher increases the degrees of freedom of the error term with the aim of increasing statistical power to test their hypotheses of interest. Despite this approach being widely adopted and explicitly recommended by some of the most widely cited statistical textbooks aimed at biologists, here we argue that (except in highly specialized circumstances that we can identify) the hoped-for improvement in statistical power will be small or non-existent, and there is likely to be much reduced reliability of the statistical procedures through deviation of type I error rates from nominal levels. We thus call for greatly reduced use of test-qualified pooling across experimental biology, more careful justification of any use that continues, and a different philosophy for initial selection of statistical models in the light of this change in procedure.
School of Biological Science, University of Edinburgh, Edinburgh EH14 4AJ, UK.
Nick
N
Colegrave
School of Biology, University of St Andrews, St Andrews KY16 9TH, UK graeme.ruxton@st-andrews.ac.uk.
Graeme D
http://orcid.org/0000-0001-8943-6609
GD
Ruxton
D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D015203:Reproducibility of Results; D012107:Research Design
experimental design; model simplification; pseudoreplication