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SOURCE: Little R. Seminal Ideas and Controversies in Statistics 2025
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MEDIAINFO
Textbook in PDF format Statistics has developed as a field through seminal ideas and fascinating controversies. Seminal Ideas and Controversies in Statistics concerns a wide-ranging set of 15 important statistical topics, grouped into three general areas: philosophical approaches to statistical inference, important statistical methodology for applications, and topics on statistical design, focusing on the role of randomization. The key papers on each topic are discussed with commentaries to help explain them. The goal is to expand reader knowledge of the statistics literature and encourage a historical perspective. Features. Discusses several important ideas in the history of statistics, including the likelihood principle, Bayes vs. frequentist approaches to inference, alternative approaches to least squares regression, shrinkage estimation, hypothesis testing, and multiple comparisons. Provides a deeper understanding and appreciation of the history of statistics. Discusses disagreements in the literature, which makes for interesting reading. Gives guidance on various aspects of statistics research by reading good examples in the literature. Promotes the use of good English style in the presentation of statistical ideas, by learning from well-written papers. Includes an appendix of style tips on writing statistical papers. This book is aimed at researchers and graduate students in statistics and biostatistics, who are interested in the history of statistics and would like to deepen their understanding of seminal ideas and controversies. It could be used to teach a special topics course or useful for any researchers keen to understand the subject better and improve their statistical presentation skills. Preface. Acknowledgments. Part I Statistical Inference. Fisher and the Method of Maximum Likelihood. To C or Not to C, That is the Question. Frequentist Flaps: Hypothesis Testing, Significance Testing, or Something Else? Fiducial Inference and the Behrens-Fisher Problem. Do You Like the Likelihood Principle? A Bayesian/Frequentist Compromise: Calibrated Bayes. Part II Statistical Methods. Baseball Averages, Foreign Cars, and Shrinkage Estimation. Alternatives to Least Squares in Regression. Multiple Perspectives on Multiple Comparisons. Generalized Estimating Equations. The Bootstrap and Bayesian Monte-Carlo Methods. Exploratory Data Analysis and Data Science. Part III Topics in Design. Randomization in Survey Sampling. Randomized Clinical Trials and the Neyman/Rubin Causal Model. Propensity Score Methods. Appendix: Twenty Style and Grammar Tips for Statistics Writing. References. Author Index. Subject Index
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