The screening of many endpoints when comparing groups from different strains, searching for some statistically significant difference, raises the multiple comparisons problem in its most severe form. Using the 0.05 level to decide which of the many endpoints' differences are statistically significant, the probability of finding a difference to be significant even though it is not real increases far beyond 0.05. The traditional approach to this problem has been to control the probability of making even one such error--the Bonferroni procedure being the most familiar procedure achieving such control. However, the incurred loss of power stemming from such control led many practitioners to neglect multiplicity control altogether. The False Discovery Rate (FDR), suggested by Benjamini and Hochberg [J Royal Stat Soc Ser B 57 (1995) 289], is a new, different, and compromising point of view regarding the error in multiple comparisons. The FDR is the expected proportion of false discoveries among the discoveries, and controlling the FDR goes a long way towards controlling the increased error from multiplicity while losing less in the ability to discover real differences. In this paper we demonstrate the problem in two studies: the study of exploratory behavior [Behav Brain Res (2001)], and the study of the interaction of strain differences with laboratory environment [Science 284 (1999) 1670]. We explain the FDR criterion, and present two simple procedures that control the FDR. We demonstrate their increased power when used in the above two studies.