Bayesian Methods for Ecology by Michael A. McCarthy

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By Michael A. McCarthy

The curiosity in utilizing Bayesian tools in ecology is expanding, besides the fact that many ecologists have trouble with accomplishing the mandatory analyses. McCarthy bridges that hole, utilizing a transparent and available sort. The textual content additionally contains case experiences to illustrate mark-recapture research, improvement of inhabitants types and using subjective judgement. the benefits of Bayesian equipment, also are defined right here, for instance, the incorporation of any correct previous details and the facility to evaluate the facts in favour of competing hypotheses. unfastened software program is out there in addition to an accompanying web-site containing the information records and WinBUGS codes. Bayesian equipment for Ecology will entice educational researchers, top undergraduate and graduate scholars of Ecology.

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Small wonder that students have trouble understanding hypothesis tests. ’ Carver (1978) ‘. . significance testing should be eliminated; it is not only useless, it is also harmful . ’ Cohen (1994) ‘. . hypothesis testing does not tell us what we want to know . . ’ I recommend that ecologists largely stop using it in favour of the methods discussed in the remainder of this chapter. One of these methods is Bayesian statistics. Given the problems with null hypothesis testing and its prevalence in ecology, one might ask how the discipline has managed to progress (Dennis, 1996).

Readers who are uncomfortable with mathematics may look at the above equation and decide that they can never solve those sorts of problems and decide that Bayesian methods are too hard. The complexity of the equation should not be discouraging because in most cases it is impossible to solve, regardless of a person’s mathematical skills. Fortunately, software is available so users do not need to evaluate or even construct the integral. 3), the denominator simply acts as a scaling constant, because it is the same for all possible values for the parameter H.

These problems are mainly due to how the method is implemented, rather than the basis of the method. In summary, errors in the use of null hypothesis testing include: 1. using silly null hypotheses; 2. believing that the p-value is the probability that the null hypothesis is true; 3. interpreting large p-values as evidence that the null hypothesis is true (a sub-set of point 2); 4. ignoring statistical power (related to point 3); 5. 05, despite power being ignored, so the type-II error rate is unknown; and 6.

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