AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons

• Published in: Behavioral ecology and sociobiology Vol. 65; no. 1; pp. 23 - 35
• Main Authors: Burnham, Kenneth P, Anderson, David R, Huyvaert, Kathryn P
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Berlin/Heidelberg : Springer-Verlag 2011; Springer; Springer-Verlag; Springer Nature B.V
• Subjects: AIC; Animal Ecology; Behavioral biology; Behavioral Sciences; Biomedical and Life Sciences; Ecological modeling; Empirical evidence; Entropy; Evidence; Inference; Kullback-Leibler information; Life Sciences; Male animals; Mating behavior; Model averaging; Model likelihoods; Model probabilities; Model selection; Modeling; Multimodel inference; Null hypothesis; P values; Probabilities; REVIEW; Statistical inference; Statistics; Variance analysis; Zoology; Model likelihoods; Model probabilities; Model selection; Model averaging; Evidence; AIC; Kullback–Leibler information; Multimodel inference
• ISSN: 0340-5443; 1432-0762
• DOI: 10.1007/s00265-010-1029-6

We briefly outline the information-theoretic (I-T) approaches to valid inference including a review of some simple methods for making formal inference from all the hypotheses in the model set (multimodel inference). The I-T approaches can replace the usual t tests and ANOVA tables that are so inferentially limited, but still commonly used. The I-T methods are easy to compute and understand and provide formal measures of the strength of evidence for both the null and alternative hypotheses, given the data. We give an example to highlight the importance of deriving alternative hypotheses and representing these as probability models. Fifteen technical issues are addressed to clarify various points that have appeared incorrectly in the recent literature. We offer several remarks regarding the future of empirical science and data analysis under an I-T framework.