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Reposted from ecolog:


Research workers in many of the life sciences are realizing the

substantial limitations of statistical tests, test statistics, arbitrary

alpha levels, P-values and the dichotomous rulings concerning

"statistical significance." The traditional approaches were developed at

the beginning of the last century and are being replaced by modern

methods that are much more useful. They provide easy-to-compute

quantities such as the probability of each hypotheses/model and measures

of formal evidence. Furthermore, simple methods allow formal inferences

(e.g., prediction/forecasting) from all the hypotheses/models in the a

priori set (multimodel inference).


I am planning to offer several 1-day courses on the information-

theoretic approaches to statistical inference during the Spring and

Summer months, 2015. These courses focus on the practical application

of these new methods and are based on Kullback-Leibler information and

Akaike's Information Criterion (AIC). The material follows the recent



Anderson, D. R. 2008. Model based inference in the life sciences:a

primer on evidence. Springer, New York, NY. 184pp.


These courses stress science and science philosophy as much as

statistical methods. The focus is on quantification and qualification of

evidence concerning alternative science hypotheses.


These courses and hosted, organized and delivered at your university,

agency, institute or training center. I have given nearly 70 of these

courses and they have been well received. The courses are informal and

discussion and debate are encouraged. Further insights can be found at




If you are interested in hosting a course at your location, please

contact me. Thank you.



David R. Anderson

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