Concerns regarding a call for pluralism of information theory and hypothesis testing
Article first published online: 5 MAR 2007
Journal of Applied Ecology
Volume 44, Issue 2, pages 456–460, April 2007
How to Cite
LUKACS, P. M., THOMPSON, W. L., KENDALL, W. L., GOULD, W. R., DOHERTY, P. F., BURNHAM, K. P. and ANDERSON, D. R. (2007), Concerns regarding a call for pluralism of information theory and hypothesis testing. Journal of Applied Ecology, 44: 456–460. doi: 10.1111/j.1365-2664.2006.01267.x
- Issue published online: 5 MAR 2007
- Article first published online: 5 MAR 2007
- Received 30 January 2006; final copy received 13 November 2006Editor: Rob Freckleton
- Akaike's information criterion;
- information theory;
- model selection;
- multimodel inference;
- null hypothesis testing;
- statistical analysis
- 1Stephens et al. (2005) argue for ‘pluralism’ in statistical analysis, combining null hypothesis testing and information-theoretic (I-T) methods. We show that I-T methods are more informative even in single variable problems and we provide an ecological example.
- 2I-T methods allow inferences to be made from multiple models simultaneously. We believe multimodel inference is the future of data analysis, which cannot be achieved with null hypothesis-testing approaches.
- 3We argue for a stronger emphasis on critical thinking in science in general and less reliance on exploratory data analysis and data dredging. Deriving alternative hypotheses is central to science; deriving a single interesting science hypothesis and then comparing it to a default null hypothesis (e.g. ‘no difference’) is not an efficient strategy for gaining knowledge. We think this single-hypothesis strategy has been relied upon too often in the past.
- 4We clarify misconceptions presented by Stephens et al. (2005).
- 5We think inference should be made about models, directly linked to scientific hypotheses, and their parameters conditioned on data, Prob(Hj | data). I-T methods provide a basis for this inference. Null hypothesis testing merely provides a probability statement about the data conditioned on a null model, Prob(data | H0).
- 6Synthesis and applications. I-T methods provide a more informative approach to inference. I-T methods provide a direct measure of evidence for or against hypotheses and a means to consider simultaneously multiple hypotheses as a basis for rigorous inference. Progress in our science can be accelerated if modern methods can be used intelligently; this includes various I-T and Bayesian methods.