Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper studies the impact of noisy signals on screening processes. It deals with a decision problem in which a decision-maker screens a set of elements based on noisy unbiased evaluations. Given that the decision-maker uses threshold strategies, we show that additional binary noise can potentially improve a screening, an effect that resembles a "lucky coin toss." We compare different noisy signals under threshold strategies and optimal ones, and we provide several characterizations of cases in which one noise is preferable over another. Accordingly so, we establish a novel method to compare noise variables using a contraction mapping between percentiles.