Ecology, Evidence, and Objectivity: In Search of a Bias-Free Methodology

Abstract

For at least the past 25 years or so, there has been a twofold sense of “crisis” in ecology. One indication of this is the spate of articles and books calling for a reformation of the discipline and bearing such titles as “The New Ecology.” On the part of practitioners, the unease concerns its theories, concepts, and methods. On the part of the general public, the unease concerns the perceived “bias” of its results. This paper is an attempt by two philosophers of science to clarify one critical methodological issue – hypothesis/model testing – and in the process to identify ways to gird the objectivity of ecological claims. What is significant about our approach is a distinction between the tasks appropriate to Bayesian Inference and Evidential Statistics – confirming hypotheses on the one hand and measuring evidence for models on the other. These two inferential paradigms are contrasted with the testing methods long-dominant in the discipline – Fisher-Neyman-Pearson Significance Testing and Popper Falsificationism – and a case made for a much greater use of Bayesian and Evidentialist Methods. In particular, it is argued that Evidential Statistics, here in the form of the likelihood ratios of competing predictive and explanatory multiple models avoids the main forms of otherwise unsettling cognitive bias. It also provides a Darwinian alternative to the “convergence” accounts of objectivity associated with the development of physics which is more appropriate to ecology. Keywords: Scientific method, Bayesian inference, evidential statistics, confirmation, evidence, significance testing, falsificationism, hypothetico-deductivism, convergence, objectivity