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Technical Report from the year 1998 in the subject Mathematics - Statistics, grade: 1.0, Technical University of Denmark (Institute for Mathematical Modeling), language: English, abstract: Most human brain imaging experiments involve a number of subjects that is unusually low by accepted statistical standards. Although there are a number of practical reasons for using small samples in neuroimaging we need to face the question regarding whether results obtained with only a few subjects will generalise to a larger population. In this contribution we address this issue using a Bayesian framework, derive confidence intervals for small samples experiments, and discuss the issue of the prior.
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Technical Report from the year 1998 in the subject Mathematics - Statistics, grade: 1.0, Technical University of Denmark (Institute for Mathematical Modeling), language: English, abstract: Most human brain imaging experiments involve a number of subjects that is unusually low by accepted statistical standards. Although there are a number of practical reasons for using small samples in neuroimaging we need to face the question regarding whether results obtained with only a few subjects will generalise to a larger population. In this contribution we address this issue using a Bayesian framework, derive confidence intervals for small samples experiments, and discuss the issue of the prior.
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On small sample experiments in neuro-imaging
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Technical Report from the year 1998 in the subject Mathematics - Statistics, grade: 1.0, Technical University of Denmark (Institute for Mathematical Modeling), language: English, abstract: Most human brain imaging experiments involve a number of subjects that is unusually low by accepted statistical standards. Although there are a number of practical reasons for using small samples in neuroimaging we need to face the question regarding whether results obtained with only a few subjects will generalise to a larger population. In this contribution we address this issue using a Bayesian framework, derive confidence intervals for small samples experiments, and discuss the issue of the prior. Artikel-Nr. 9783668438828
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