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Principles of Statistical Inference
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  • Cited by 139
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    Bickel, David R. 2019. Sharpen statistical significance: Evidence thresholds and Bayes factors sharpened into Occam's razor. Stat, Vol. 8, Issue. 1, p. e215.

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    Ward, Michael D. and Ahlquist, John S. 2018. Maximum Likelihood for Social Science.

    Robinson, Geoffrey K. 2018. What Properties Might Statistical Inferences Reasonably be Expected to Have?—Crisis and Resolution in Statistical Inference. The American Statistician, p. 1.

    Morey, Richard D. 2018. Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience. p. 1.

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Book description

In this definitive book, D. R. Cox gives a comprehensive and balanced appraisal of statistical inference. He develops the key concepts, describing and comparing the main ideas and controversies over foundational issues that have been keenly argued for more than two-hundred years. Continuing a sixty-year career of major contributions to statistical thought, no one is better placed to give this much-needed account of the field. An appendix gives a more personal assessment of the merits of different ideas. The content ranges from the traditional to the contemporary. While specific applications are not treated, the book is strongly motivated by applications across the sciences and associated technologies. The mathematics is kept as elementary as feasible, though previous knowledge of statistics is assumed. The book will be valued by every user or student of statistics who is serious about understanding the uncertainty inherent in conclusions from statistical analyses.


'A deep and beautifully elegant overview of statistical inference, from one of the towering figures who created modern statistics. This book should be essential reading for all who call themselves ‘statistician’.'

David Hand - Imperial College London

'The explanations of key concepts are written so clearly … that they may be understood even if the mathematical details are skipped.'

Source: MAA Online

'The text is very well written and gives a balanced view of the frequentist and Bayesian notions of probability, without favouring one over the other.'

Source: Journal of Applied Statistics

'… ideally suited for statisticians at all levels who want to refresh their own understanding of the theory of statistical inference without having to wade through theorems and proofs.'

Source: Biometrics

'This is a great book by a great statistician. Buy it and read it.'

Source: Journal of the American Statistical Association

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