4 August 2008
I missed Aaron Ellison and Brian Dennis's talk today at ESA about "what literate ecologists should know about statistics" (I opted for Gordon Fox's talk about diversity gradients instead). It would have been at least culturally interesting since BD is a staunch frequentist and AE is a hard-core Bayesian. (Interestingly, most statisticians have got beyond having these arguments. I apologize if either of the authors disagree with my characterization of them.) I mostly agreed with what I saw in the abstract, although I don't draw as bright a line as they appear to between "model-based" and "design-based" statistics. Here's an excerpt:
we suggest that literate ecologists at a minimum should master core statistical concepts, including probability and likelihood, principles of data visualization and reduction, fundamentals of sampling and experimental design, the difference between design-based and model-based inference, model formulation and construction, and basic programming. Because mathematics is the language of statistics, familiarity with essential mathematical tools – matrix algebra and especially calculus – is a must and will facilitate collaborations between ecologists and statisticians.
Maybe I'll post more here if they would like.
[Update August 5: Aaron said something today about publishing it in Frontiers in Ecology (IIRC).]