Paths to Statistical Fluency for Ecologists

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.