Paths to Statistical Fluency for Ecologists

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Paths to Statistical Fluency for Ecologists

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Title: Paths to Statistical Fluency for Ecologists
Author: Ellison, Aaron; Dennis, Brian

Note: Order does not necessarily reflect citation order of authors.

Citation: Ellison, Aaron M. and Brian Dennis. Forthcoming. Paths to statistical fluency for ecologists. Frontiers in Ecology and the Environment.
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Abstract: Twenty-first century ecology requires statistical fluency. Observational and experimental studies routinely gather non-Normal, multivariate data at many spatiotemporal scales. Experimental studies routinely include multiple blocked and nested factors. Ecological theories
routinely incorporate both deterministic and stochastic processes. Ecological debates frequently revolve around choices of statistical analyses. Our journals are replete with likelihood and state-space models, Bayesian and frequentist inference, complex multivariate analyses, and papers on
statistical theory and methods. We test hypotheses, model data, and forecast future environmental conditions. And many appropriate statistical methods are not automated in software packages. It is time for ecologists to understand statistical modeling well enough to construct nonstandard statistical models and apply various types of inference – estimation, hypothesis testing, model selection, and prediction – to our models and scientific questions. In short, ecologists need to
move beyond basic statistical literacy and attain statistical fluency.
Published Version: http://www.esajournals.org/loi/fron
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:2766569
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