This book is about making valid inferences from scientific data when a meaningful analysis depends on a model of the information in the data. Our general objective is to provide scientists, including statisticians, with a readable text giving practical advice for the analysis of empirical data under an information-theoretic paradigm. We first assume that an exciting scientific question has been carefully posed and relevant data have been collected, following a sound experimental design or probabilistic sampling program. Alternative hypotheses, and models to represent them, should be carefully considered in the design stage of the investigation. Often, little can be salvaged if data collection has been seriously flawed or if the question was poorly posed (Hand 1994). We realize, of course, that these issues are never as ideal as one would like. However, proper attention must be placed on the collection of data (Chatfield 1991,1995 a Anderson 2001). We stress inferences concerning the structure and function of biological systems, relevant parameters, valid measures of precision, and formal prediction.


Model Selection Global Model Biological Science True Model Candidate Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag New York, Inc. 2002

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