This book improves Meeker and Escobar (1998, Wiley) not only in terms of organization and presentation, but also in extensions and modifications to the technical material. It presents state-of-the-art, computer-based statistical methods for reliability data analysis and for test planning of industrial products. It also improves long time established techniques as it demonstrates how to apply the new graphical, numerical, or simulation-based methods to a broad range of models encountered in reliability data analysis. Bayesian methods in solving practical problems (e.g. models involving random effects or censoring that arises in reliability studies) are now incorporated where appropriate; the computations are done with WinBUGS. Ample exercises that extend and strengthen the concepts in the book are included. The criterion for integrating material in the book is that the authors have in-hand or have seen real applications for the methodology. The book is specifically geared for a one-semester course on the topic in either a statistics or engineering department at either the upper-undergraduate or beginning graduate levels. R functions and subroutines, along with an extensive list of data sets, are included on a massive web site that is meticulously maintained by the authors.