Journal Article

Alternatives to Statistical Hypothesis Testing in Ecology: A Guide to Self Teaching

Hilborn, Ray, Hobbs, N. Thompson

2006

Overview

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.

• Publisher: Ecological Society of America
• Subject: AIC / Algorithms / Applied ecology / Bayes Theorem / Bayesian / Bayesian theory / Computational Biology - methods / Computer Simulation / Data Interpretation, Statistical / Ecological modeling / ecologists / Ecology / Ecology - methods / Ecology - statistics & numerical data / Ecosystem / empirical research / information theoretic / Invited Feature: Contemporary Statistics and Ecology / likelihood / Likelihood Functions / Marine ecology / Meta analysis / Meta-Analysis as Topic / model selection / Modeling / Models, Biological / Parametric models / Population ecology / Probabilities / Research Design / statistical analysis / Statistics / weight-of-evidence