ALTERNATIVE SCIENCE HYPOTHESES AND AIC MODEL SELECTION Research workers in many fields are realizing the substantial limitations of statistical tests, test statistics, arbitrary α-levels, P-values, and dichotomous rulings concerning “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 evidence. They provide easy-to-compute quantities such at the probability of each hypothesis/model and evidence ratios. Furthermore, simple methods allow formal inference (e.g. prediction/forecasting) from all the models in an a priori set (“multimodel inference”).
I am planning to offer several 1-day courses on the Information- Theoretic approaches to statistical inference during the fall of 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 textbook, 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 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 >60 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 www.aic-overview.com/aic-overview.pdf if are interested in hosting a course at your location, please contact me. Thank you. David R. Anderson quietanderson – at - yahoo.com
