Statistical Hypothesis Testing in Context: Volume 52
Reproducibility, Inference, and Science

This coherent guide equips applied statisticians to make good choices and proper interpretations in real investigations facing real data.

Michael P. Fay (Author), Erica H. Brittain (Author)

9781108423564, Cambridge University Press

Hardback, published 5 May 2022

448 pages
25.9 x 18.2 x 2.9 cm, 0.98 kg

'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

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.

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.

Subject Areas: Probability & statistics [PBT]