Statistical hypothesis testing in context: reproducibility, inference, and science

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.