IUCN / SSC Cat Specialist Group - Digital Cat Library |
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| Ellison, A.M. | |
| An introduction to Bayesian inference for ecological research and environmental decision-making | |
| 1996 Ecological Applications (6): 1036-1046 | |
In our statistical practice, we ecologists work comfortably within the hypothetico-deductive epistemology of Popper and the frequent statistical methodology of Fisher. Consequently, our null hypotheses do not often take into account pre-existing data and do not require parameterisation, our experiments demand large sample sizes, and we rarely use results from one experiment to predict the outcomes of future experiments. Comparative statistical statements such as "we reject the null hypothesis at the 0.05 level," which reflect the likelihood of our data given our hypothesis, are of little use in communicating our results to non-specialists or in describing the degree of certitude we have in our conclusions. In contrast, Bayesian statistical inference requires the explicit assignment of prior probabilities, based on existing information, to the outcomes of experiments. Such an assignment forces the parameterisation of null and alternative hypotheses. The results of these experiments, regardless of sample size, then can be used to compute posterior probabilities of our hypotheses given the available data. Inferential conclusions in a Bayesian mode also are more meaningful in environmental policy discussions: e.g., "our experiments indicate that there is a 95% probability that acid deposition will affect north-eastern conifer forests." Based on comparisons with current statistical practice in ecology, I argue that a "Bayesian ecology" would (a) make better use of pre-existing data; (b) allow stronger conclusions to be drawn from large-scale experiments with few replicates; and (c) be more relevant to environmental decision-making. |
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