The talk will consist of three parts, each based on a different research paper, all concern a decision maker who uses noisy unbiased assessments to screen elements from a general set: the first part, based on "A Bias of Screening" (AER: Insights, 2019), shows that stricter screening not only reduces the number of accepted elements, but possibly reduces their average expected value. The second part, based on ''Screening Dominance: A Comparison of Noisy Signals" (forthcoming AEJ: Microeconomics) shows that one can actually generate `lucky coins' as additional binary noise can strictly improve a screening process. We also provide a comparison of different noisy signals under threshold (screening) strategies and optimal ones, and depict several partial characterizations of cases in which one noise is preferable over another. The third and last part, based on a working paper entitled ''Sequential Screening", would try to answer the question of why we use sequential screening. In addition, we would  show that one-stage screening is possibly preferable to multi-stage screening.