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Research workers in many fields are realizing the substantial limitations of statistical significance tests, test statistics, arbitrary alpha levels, P-values, and dichotomous rulings concerning so-called "statistical significance." These traditional approaches were developed at the beginning of the last century and are being replaced by modern methods that are much more useful. These methods rely on the concept of information loss and formal measures of evidence. They provide easy-to-compute quantities such as the probability of each hypothes/model, given the data and evidence ratios. Furthermore, simple methods allow formal inference (e.g., prediction/forecasting) from all the models in an a priori set "ultimodel inference").



I am planning of offer several 2-day courses on the Information-Theoretic (I-T) approaches to statistical inference during the upcoming summer months. These courses focus on the practical application of these new methods and are based on Kullback-Leibler information and Akaike's information criteria (AIC). The material follows the recent textbook,


Anderson, D. R. 2008. Model based inference in the life sciences: a primer on evidence. Springer, New York, NY. 184pp.


A copy of this book and other material are included in the registration fee. These courses stress science and science philosophy as much as statistical methods. The focus in on quantification and qualification of formal evidence concerning alternative science hypotheses.


These courses can be hosted, organized, and delivered at your university, agency, institute, or training center. I have given nearly 70 of these courses and they have been very 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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