Practical advice on variable selection and reporting using Akaike information criterion

Chris Sutherland*, Darragh Hare, Paul Johnson, Daniel Linden, Robert Montgomery, Egil Droge

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
13 Downloads (Pure)


The various debates around model selection paradigms are important, but in lieu of a consensus, there is a demonstrable need for a deeper appreciation of existing approaches, at least among the end-users of statistics and model selection tools. In the ecological literature, the Akaike information criterion (AIC) dominates model selection practices, and while it is a relatively straightforward concept, there exists what we perceive to be some common misunderstandings around its application. Two specific questions arise with surprising regularity among colleagues and students when interpreting and reporting AIC model tables. The first is related to the issue of ‘pretending’ variables, and specifically a muddled understanding of what this means. The second is related to p-values and what constitutes statistical support when using AIC. There exists a wealth of technical literature describing AIC and the relationship between p-values and AIC differences. Here, we complement this technical treatment and use simulation to develop some intuition around these important concepts. In doing so we aim to promote better statistical practices when it comes to using, interpreting and reporting models selected when using AIC.
Original languageEnglish
JournalProceedings of the Royal Society B: Biological Sciences
Issue number2007
Publication statusPublished - 27 Sept 2023


  • P-value
  • Information criterion
  • Ecology
  • Variable selection
  • Model selection


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