
Statistical Hypothesis Testing in Context: Volume 52
Reproducibility, Inference, and Science
Cambridge University Press
Published on 5. May 2022
Book
Hardback
448 pages
978-1-108-42356-4 (ISBN)
Description
Fay and Brittain present statistical hypothesis testing and compatible confidence intervals, focusing on application and proper interpretation. The emphasis is on equipping applied statisticians with enough tools - and advice on choosing among them - to find reasonable methods for almost any problem and enough theory to tackle new problems by modifying existing methods. After covering the basic mathematical theory and scientific principles, tests and confidence intervals are developed for specific types of data. Essential methods for applications are covered, such as general procedures for creating tests (e.g., likelihood ratio, bootstrap, permutation, testing from models), adjustments for multiple testing, clustering, stratification, causality, censoring, missing data, group sequential tests, and non-inferiority tests. New methods developed by the authors are included throughout, such as melded confidence intervals for comparing two samples and confidence intervals associated with Wilcoxon-Mann-Whitney tests and Kaplan-Meier estimates. Examples, exercises, and the R package asht support practical use.
Reviews / Votes
'A necessary book for the applied statistician seeking to understand the theoretical underpinnings of statistical methods and for graduate students knowledgeable about statistical theory but lacking experience in application. The book is chock full of challenging examples that point to the complexities of choice of method. A particularly valuable feature of the book is the authors' description of competing methods coupled with their clarity in explaining and justifying why they prefer one method over others. Fay and Brittain should sit on every statistician's bookshelf.' Janet Wittes, WCG Statistics Collaborative 'Good statistical hypothesis testing and confidence interval construction involves mathematical aspects of finding a good test given a probability model and scientific aspects of determining the appropriateness of a probability model for answering a scientific question. This book provides a lucid discussion of both these mathematical and scientific aspects with compelling scientific examples. I most highly recommend this book.' Dylan Small, University of Pennsylvania 'Congratulations to Fay and Brittain for this wonderful reference book that does what its somewhat unusual title suggests: puts hypothesis testing in the context of science. The vast coverage of topics, extensive bibliography and notes, and easy to understand explanations make 'Statistical Hypothesis Testing in Context: Reproducibility, Inference, and Science' an indispensable tool in the arsenal of any applied or theoretical statistician or biostatistician. I enthusiastically recommend buying the book!' Michael A. Proschan, National Institute of Allergy and Infectious DiseasesMore details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Illustrations
Worked examples or Exercises
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 29 mm
Weight
1032 gr
ISBN-13
978-1-108-42356-4 (9781108423564)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Michael P. Fay | Erica H. Brittain
Statistical Hypothesis Testing in Context: Volume 52
Reproducibility, Inference, and Science
E-Book
05/2022
Cambridge University Press
€52.49
Available for download

E-Book
04/2022
Cambridge University Press
€52.49
Available for download
Persons
Michael P. Fay is a Mathematical Statistician at the National Institute of Allergy and Infectious Diseases, and previously worked at the National Cancer Institute. He has served as associate editor for Biometrics, and is currently an associate editor for Clinical Trials and a Fellow of the American Statistical Association. He is a co-author on over 100 papers in statistical and medical journals and has written and maintains over a dozen R packages on CRAN.
Content
1. Introduction; 2. Theory of tests, p-values, and confidence intervals; 3. From scientific theory to statistical hypothesis test; 4. One sample studies with binary responses; 5. One sample studies with ordinal or numeric responses; 6. Paired data; 7. Two sample studies with binary responses; 8. Assumptions and hypothesis tests; 9. Two sample studies with ordinal or numeric responses; 10. General methods for creating decision rules; 11. K-Sample studies and trend tests; 12. Clustering and stratification; 13. Multiplicity in testing; 14. Testing from models; 15. Causality; 16. Censoring; 17. Missing data; 18. Group sequential and related adaptive methods; 19. Testing fit, equivalence, and non-inferiority; 20. Power and sample size.