Statistical Methods

Kenneth J. Berry and Janis E. Johnston

Chapter Chapter 2 in Statistical Methods: Connections, Equivalencies, and Relationships, 2023, pp 13-53 from Springer

Abstract: Abstract Chapter 2 describes two models of statistical inference: the population model first put forward by J. Neyman and E. Pearson in 1928 and the permutation model developed by R.A Fisher, R.C. Geary, T. Eden, F. Yates, H. Hotelling, M. R. Pabst, and E.J.G. Pitman in the 1920s and 1930s. First, the Neyman–Pearson population model of statistical inference is described. Second, the assumptions underlying the population model of independence, random sampling, normality, homogeneity of variance, and homogeneity of variance are examined. Third, a historical perspective on the Fisher–Pitman permutation model of statistical inference is presented. Fourth, the two models of statistical inference are compared. Fifth, the differences between the Neyman–Pearson population model and the Fisher–Pitman permutation model are illustrated with the analysis of a small dataset. Finally, exact and Monte Carlo permutation methods are described and illustrated utilizing a historical dataset.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-41896-9_2

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DOI: 10.1007/978-3-031-41896-9_2

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