Evidential statistics as a statistical modern synthesis to support 21st century science

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• 作者：Mark L. Taper ; José Miguel Ponciano
• 关键词：Bayesian statistics ; Error statistics ; Evidential statistics ; Information criteria ; Likelihoodism ; Statistical inference
• 刊名：Population Ecology
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：58
• 期：1
• 页码：9-29
• 全文大小：775 KB
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• 作者单位：Mark L. Taper (1)
  José Miguel Ponciano (2)
  
  1. Ecology Department, Montana State University, Bozeman, MT, 59717-3460, USA
  2. Department of Biology, University of Florida, Gainesville, FL, 32611-8525, USA
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Life Sciences
  Ecology
  Zoology
  Plant Sciences
  Evolutionary Biology
  Behavioural Sciences
  Forestry
  
• 出版者：Springer Japan
• ISSN：1438-390X

文摘

During the 20th century, population ecology and science in general relied on two very different statistical paradigms to solve its inferential problems: error statistics (also referred to as classical statistics and frequentist statistics) and Bayesian statistics. A great deal of good science was done using these tools, but both schools suffer from technical and philosophical difficulties. At the turning of the 21st century (Royall in Statistical evidence: a likelihood paradigm. Chapman & Hall, London, 1997; Lele in The nature of scientific evidence: statistical, philosophical and empirical considerations. The University of Chicago Press, Chicago, pp 191–216, 2004a), evidential statistics emerged as a seriously contending paradigm. Drawing on and refining elements from error statistics, likelihoodism, Bayesian statistics, information criteria, and robust methods, evidential statistics is a statistical modern synthesis that smoothly incorporates model identification, model uncertainty, model comparison, parameter estimation, parameter uncertainty, pre-data control of error, and post-data strength of evidence into a single coherent framework. We argue that evidential statistics is currently the most effective statistical paradigm to support 21st century science. Despite the power of the evidential paradigm, we think that there is no substitute for learning how to clarify scientific arguments with statistical arguments. In this paper we sketch and relate the conceptual bases of error statistics, Bayesian statistics and evidential statistics. We also discuss a number of misconceptions about the paradigms that have hindered practitioners, as well as some real problems with the error and Bayesian statistical paradigms solved by evidential statistics. Keywords Bayesian statistics Error statistics Evidential statistics Information criteria Likelihoodism Statistical inference