Recently, the idea of multiple comparisons has been criticized because of its lack of power in datasets with a large number of treatments. Many family-wise error corrections are far too restrictive when large quantities of comparisons are being made. At the other extreme, a test like the least significant difference does not control the family-wise error rate, and therefore is not restrictive enough to identify true differences. A solution lies in multiple testing. The false discovery rate (FDR) uses a simple algorithm and can be applied to datasets with many treatments. The current research compares the FDR method to Dunnett's test using agronomic data from a study with 196 varieties of dry beans. Simulated data is used to assess type I error and power of the tests. In general, the FDR method provides a higher power than Dunnett's test while maintaining control of the type I error rate.