This book focuses on hybrid censoring, a specific but important topic in censoring methodology, which has numerous applications. Applied statisticians in many fields must frequently analyze time to event data. The statistical tools presented in this book are applicable to data from medicine, biology, public health, epidemiology, engineering, economics, and demography. This book presents why the analysis of censored data is important from an applied point of view as well as from a theoretical point of view. Extensive data sets from life-testing experiments where these forms of data occur naturally are described. AThe analysis of survival experiments is complicated by issues of censoring, where an individual's life length is known to occur only in a certain period of time, and by truncation, where individuals enter the study only if they survive a sufficient length of time or individuals are included in the study only if the event has occurred by a given date. The existing literature on censoring methodology, life-testing procedures or lifetime data analysis provide only some hybrid censoring schemes but do not spend a significant amount of time to detail the methodologies, ideas and statistical inferential methods for hybrid censoring. This book fills this gap and provides valuable information on these topics. Presents many numerical examples to adequately illustrate all the inferential methods discussed Provides open problems and possible directions for future work Reviews developments pertaining to Type-II HCS and includes the most recent research and trends Explains why the hybrid censored sampling is important, provides detail in using HCS under different settings and the designs of HCS Includes R code on website for ease of use