This book is intended for students and practitioners who have had a calculus-based statistics course and who have an interest in safety considerations such as reliability, strength, and duration-of-load or service life. Many persons studying statistical science will be employed professionally where the problems encountered are obscure, what should be analyzed is not clear, the appropriate assumptions are equivocal, and data are scant. Yet tutorial problems of this nature are virtually never encountered in coursework. In this book there is no disclosure with many of the data sets what type of investigation should be made or what assumptions are to be used. Most reliability practitioners will be employed where personal interaction between disciplines is a necessity. A section is included on communication skills to facilitate model selection and formulation based on verifiable assumptions, rather than favorable conclusions. However, whether the answer is "right" can never be ascertained. Past and current applications of stochastic modeling to life-length can only be a guide for future adaptations under different conditions, with new materials in unknown usages. This book unifies the study of cumulative-damage distributions, namely, Wald and Tweedie (i.e., inverse-Gaussian and its reciprocal) with "fatigue-life." These distributions are most useful when the coefficient-of-variation is more appropriate than is the variance as a measure of dispersion. It is shown, uniquely, that the same hyperbolic-sine transformation of each life length variate has a Chi-square one-df distribution. This property is useful in the sample statistics. These IHRA distributions realistically model life-length, strength or duration of load under linear cumulative damage and can be combined as approximations in non-linear situations. Sam C. Saunders has served as a research engineer for 17 years at the Boeing Scientific Research Laboratories, 20 years as a consultant to the Advisory Committee for Nuclear Safeguards, 10 years as a consultant to NIST, was a principal in the consulting firms Mathematical Analysis Research Corporation and Scientific Consulting Service; and was for 26 years a professor of Applied Mathematics/Statistics at Washington State University. He is a Fellow of the American Statistical Association and a former editor of Technometrics.