Behavioral Ecology Advance Access published online on November 2, 2009
Behavioral Ecology, doi:10.1093/beheco/arp137
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Review |
Changing philosophies and tools for statistical inferences in behavioral ecology
a Department of Evolutionary Ecology, Estación Biológica de Doñana-CSIC, c/Americo Vespucio, s/n, 41092, Seville, Spain b Department of Biology, Queen's University, Kingston, ON, K7L 3N6, Canada c Program in Ecology, Evolution and Conservation Biology, Department of Biology, University of Nevada, Reno, NV 89557, USA d Department of Systematic Zoology and Ecology, Eötvös Loránd University, Pázmány Péter sétány 1/C, H-1117, Budapest, Hungary e Department of Psychology, University of Alberta, Edmonton, Alberta T6G 2E9, Canada f Department of Biology, University of Bergen, PO Box 7803, N-5020, Bergen, Norway g Department of Evolutionary Studies of Biosystems, The Graduate University for Advanced Studies, Hayama, Miura-gun, Zushi, Kanagawa 240-0193, Japan h National Evolutionary Synthesis Center, 2024 W. Main Street, Suite A200, Durham, NC 27705-4667, USA i Department of Ecology and Evolutionary Biology, University of California, Life Sciences Building, 621 Charles E. Young Drive South, Los Angeles, CA 90095-1606, USA j Max Planck Institute for Ornithology, Eberhard-Gwinner-Str. 5, 82319 Seewiesen, Germany k Department of Zoology, University of Melbourne, Victoria 3010, Australia l Department of Zoology, University of Otago, 340 Great King Street, PO Box 56, Dunedin, New Zealand
Address correspondence to L.Z. Garamszegi. E-mail: laszlo.garamszegi{at}ebd.csic.es.
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Abstract |
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Recent developments in ecological statistics have reached behavioral ecology, and an increasing number of studies now apply analytical tools that incorporate alternatives to the conventional null hypothesis testing based on significance levels. However, these approaches continue to receive mixed support in our field. Because our statistical choices can influence research design and the interpretation of data, there is a compelling case for reaching consensus on statistical philosophy and practice. Here, we provide a brief overview of the recently proposed approaches and open an online forum for future discussion (https://bestat.ecoinformatics.org/). From the perspective of practicing behavioral ecologists relying on either correlative or experimental data, we review the most relevant features of information theoretic approaches, Bayesian inference, and effect size statistics. We also discuss concerns about data quality, missing data, and repeatability. We emphasize the necessity of moving away from a heavy reliance on statistical significance while focusing attention on biological relevance and effect sizes, with the recognition that uncertainty is an inherent feature of biological data. Furthermore, we point to the importance of integrating previous knowledge in the current analysis, for which novel approaches offer a variety of tools. We note, however, that the drawbacks and benefits of these approaches have yet to be carefully examined in association with behavioral data. Therefore, we encourage a philosophical change in the interpretation of statistical outcomes, whereas we still retain a pluralistic perspective for making objective statistical choices given the uncertainties around different approaches in behavioral ecology. We provide recommendations on how these concepts could be made apparent in the presentation of statistical outputs in scientific papers.
Key words: BeStat, Bonferroni correction, frequentist approach, information theoretic approach, measurement error, model selection, P value, prior, statistical power.
Received 5 December 2008; revised 29 July 2009; accepted 29 August 2009.