Nonresponse is a prominent problem in sample surveys. At face value, it reduces the trust in survey estimates. Nonresponse undermines the probability-based inferential mechanism and introduces the potential for nonresponse bias. In addition, there are other important consequences. The effort to limit increasing nonresponse has led to higher survey costs—allocation of greater resources to measure and reduce nonresponse. Nonresponse has also led to greater survey complexity in terms of design, implementation, and processing of survey data, such as the use of multiphase and responsive designs. The use of mixed-mode and multiframe designs to address nonresponse increases complexity but also introduces other sources of error. Surveys have to rely to a greater extent on statistical adjustments and auxiliary data. This article describes the major consequences of survey nonresponse, with particular attention to recent years.