Peter M. B. Cahusac
Evidence-Based Statistics: An Introduction to the Evidential Approach - from Likelihood Principle to Statistical Practice
Evidence-Based Statistics: An Introduction to the Evidential Approach - from Likelihood Principle to Statistical Practice
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Evidence-Based Statistics: An Introduction to the Evidential Approach provides a comprehensive and thorough guide to the evidential approach in statistics, using likelihood ratios and making calculations more straightforward. It is conceptually easier to grasp and offers a useful addition to any statistician's bag of tricks, with step-by-step instructions and R statistical code for commonly used analyses. Recommended for undergraduate and graduate students, active researchers, and professionals.
Format: Hardback
Length: 256 pages
Publication date: 06 October 2020
Publisher: John Wiley and Sons Ltd
Evidence-Based Statistics: An Introduction to the Evidential Approach – from Likelihood Principle to Statistical Practice is a comprehensive and thorough guide to the evidential approach in statistics. The approach uses likelihood ratios, rather than the probabilities used by other statistical inference approaches, making it conceptually easier to grasp and the calculations more straightforward to perform. This book explains how to express data in terms of the strength of statistical evidence for competing hypotheses.
Despite its mathematical precision and statistical validity, the evidential approach is currently underused. Evidence-Based Statistics is an accessible and practical text filled with examples, illustrations, and exercises. Additionally, the companion website complements and expands on the information contained in the book. While the evidential approach is unlikely to replace probability-based methods of statistical inference, it provides a useful addition to any statistician's bag of tricks.
In this book, readers will learn how to calculate statistical evidence for commonly used analyses in a step-by-step fashion. Analyses covered include t tests, ANOVA (one-way, factorial, between- and within-participants, mixed), categorical analyses (binomial, Poisson, McNemar, rate ratio, odds ratio, data that is too good to be true, multi-way tables), correlation, regression, and nonparametric analyses (one sample, related samples, independent samples, multiple independent samples, permutation and bootstraps). Equations are given for all analyses, and R statistical code is provided for many of the analyses. Sample size calculations for evidential probabilities of misleading and weak evidence are explained, along with useful techniques like Matthews's critical value.
Overall, Evidence-Based Statistics: An Introduction to the Evidential Approach – from Likelihood Principle to Statistical Practice is an invaluable resource for anyone interested in applying the evidential approach to statistical analysis. Its comprehensive coverage, practical examples, and user-friendly approach make it an essential tool for researchers, practitioners, and students in the field of statistics.
Weight: 474g
Dimension: 160 x 237 x 20 (mm)
ISBN-13: 9781119549802
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