Scientists often propose hypotheses based on patterns seen in data. However, if a scientist tests a hypothesis using the same data that suggested the hypothesis, then that scientist has violated a rule of science. The rule is: test hypotheses with independent data. This rule may sound so obvious as to be hardly worth mentioning. In fact, this mistake occurs frequently, especially when analyzing large data sets. Among the many pitfalls in statistics, screening is particularly serious. Screening is the process of evaluating a property for a large number of samples and then selecting samples in which that property is extreme. Screening is closely related to data fishing, data dredging, or data snooping. After a sample has been selected through screening, classical hypothesis tests exhibit selection bias. Quantifying the effect of screening often reveals that it creates biases that are substantially larger than one might guess. This chapter explains the concept of screening and illustrates it through examples from selecting predictors, interpreting correlation maps, and identifying change points.