Social psychologists have long recognized the power of statisticized groups. When individual judgments about some fact (e.g., the unemployment rate for next quarter) are averaged together, the average opinion is typically more accurate than most of the individual estimates, a pattern often referred to as the wisdom of crowds. The accuracy of averaging also often exceeds that of the individual perceived as most knowledgeable in the group. However, neither averaging nor relying on a single judge is a robust strategy; each performs well in some settings and poorly in others. As an alternative, we introduce the select-crowd strategy, which ranks judges based on a cue to ability (e.g., the accuracy of several recent judgments) and averages the opinions of the top judges, such as the top 5. Through both simulation and an analysis of 90 archival data sets, we show that select crowds of 5 knowledgeable judges yield very accurate judgments across a wide range of possible settings-the strategy is both accurate and robust. Following this, we examine how people prefer to use information from a crowd. Previous research suggests that people are distrustful of crowds and of mechanical processes such as averaging. We show in 3 experiments that, as expected, people are drawn to experts and dislike crowd averages-but, critically, they view the select-crowd strategy favorably and are willing to use it. The select-crowd strategy is thus accurate, robust, and appealing as a mechanism for helping individuals tap collective wisdom.