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The book provides a comprehensive theoretical foundation in hypothesis testing and decision theory, tailored for practitioners and trainers in statistics and biostatistics. It emphasizes the shift towards distribution-free permutation methods as the primary approach for hypothesis testing, driven by the need for powerful statistical tools, regulatory demands for exact significance levels, and the complexities of real-world data. While it advocates for permutation tests, it also acknowledges the continued relevance of certain robust parametric tests like Student's t in statistical practice.
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Permutation, Parametric, and Bootstrap Tests of Hypotheses, Phillip I. Good
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- Pubblicato
- 2010
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