A method that has proved extremely successful in the history of science is to take ideas about how nature works, whether obtained deductively or inductively, and translate them into quantitative statements. These statements, then, can be compared with the realizations of the processes under study. The main two schools of statistical thought, frequentist and Bayesian statistics, do not address the question of evidence explicitly. This chapter summarizes various approaches to quantifying scientific evidence and compares them to Bayesian and frequentist statistics. It discusses ideas on model adequacy and model selection in the context of quantifying evidence and explores the role and scope of the use of expert opinion. Replication is usually highly desirable but in many ecological experiments difficult to obtain. How can one quantify evidence obtained from unreplicated data? Nuisance parameters, composite hypotheses, and outliers are realities of nature. Finally, the chapter raises a number of important unresolved issues, such as using evidence to make decisions without resorting to subjective probability.
Keywords: science; scientific evidence; frequentist statistics; Bayesian statistics; model adequacy; model selection; expert opinion; replication; nuisance parameters; outliers
Chapter. 9855 words.
Subjects: Animal Pathology and Diseases
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