title: Alternatives to statistical hypothesis testing in ecology: a guide to self teaching.
abstract: Statistical methods emphasizing formal hypothesis testing have dominated the analyses used by ecologists to gain insight from data. Here, we review alternatives to hypothesis testing including techniques for parameter estimation and model selection using likelihood and Bayesian techniques. These methods emphasize evaluation of weight of evidence for multiple hypotheses, multimodel inference, and use of prior information in analysis. We provide a tutorial for maximum likelihood estimation of model parameters and model selection using information theoretics, including a brief treatment of procedures for model comparison, model averaging, and use of data from multiple sources. We discuss the advantages of likelihood estimation, Bayesian analysis, and meta-analysis as ways to accumulate understanding across multiple studies. These statistical methods hold promise for new insight in ecology by encouraging thoughtful model building as part of inquiry, providing a unified framework for the empirical analysis of theoretical models, and by facilitating the formal accumulation of evidence bearing on fundamental questions.
authors.affiliation: Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado 80523, USA. nthobbs@nrel.colostate.edu
authors.forename: N Thompson
authors.initials: NT
authors.lastname: Hobbs
authors.forename: Ray
authors.initials: R
authors.lastname: Hilborn
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004463:Ecology / Q000379:methods* / Q000706:statistics & numerical data*; D017753:Ecosystem; D016013:Likelihood Functions; D015201:Meta-Analysis as Topic; D008954:Models, Biological; D012107:Research Design