Statistical Hypothesis Testing in Context
Statistical Hypothesis Testing in Context
Erica H. Brittain, National Institute of Allergy and Infectious Diseases
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.
- Encapsulates 60 years of experience with consequential applications in a unified presentation of the most useful methods and how to evaluate and modify them
- Presents ideas of causal inference used in clinical trials for decades together with a modern account of causal ideas using potential outcomes
- Provides R package asht with functions for immediate application of both new and classical methods
- Includes 95 end-of-chapter exercises, and over 100 examples that illustrate key concepts
Awards
Finalist 2023 PROSE Mathematics and Statistics Award, Association of American Publishers
Reviews & endorsements
'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 Diseases
Product details
No date availableHardback
9781108423564
448 pages
259 × 182 × 29 mm
0.98kg
Table of Contents
- 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.
