We propose and test a novel decision-making bias called “outlier neglect.” In general, when evaluating people, places, and opportunities with many features, people focus on the average of those features. For example, when hiring a team, people want the average performance of the team to be high, so they try to hire individuals who perform well on average. But in many cases, the relevant metric is not the average, but the best – for example, when a pharmaceutical company tests different types of malaria drugs, their success depends on the best-performing drug (not the average) because they can scale up the best and ignore lesser performers. We propose that evaluators neglect the importance of outliers in such cases and instead focus on portfolio averages. In some cases, this leads to suboptimal outcomes (e.g. in the drug scenario above, or it may be better to hire someone to join a team who is amazing at one specialized task than someone who is higher on average). We test this bias and show that across many contexts – including personnel selection, creative idea generation, consumer decisions, and investment strategy – people neglect outliers in favor of the average, leading to suboptimal decisions.…Read More