In quantitative proteomics, the false discovery rate (FDR), can be defined as the number of false positives within statistically significant changes in expression. False positives accumulate during the simultaneous testing of expression changes across hundreds or thousands of protein or peptide species when univariate tests such as the Student’s t-Test are employed. Currently, most researchers rely solely on the estimation of p-values and a significance threshold, but this approach may result in false positives as it does not account for the multiple testing effect. For each species, a measure of significance in terms of the FDR can be calculated, producing individual q-values. The q-value maintains power by allowing the investigator to achieve an acceptable level of true or false positives within the calls of significance. The q-value approach relies on the use of the correct statistical test for the experiment design. In this situation, a uniform p-value frequency distribution when there are no differences in expression between two samples should be obtained. Here we report a bias in p-value distribution in the case of a three-dye DIGE experiment where no changes in expression are occurring. The bias was shown to arise from correlation in the data from the use of a common internal standard. With a two-dye schema, such bias was removed, enabling the application of the q-value to two different proteomic studies. In the case of the first study, we demonstrate that 80% of calls of significant by the more traditional method are false positives. In the second, we show that calculating the q-value gives the user control over the FDR. These studies demonstrate the power and ease of use of the q-value in correcting for multiple testing. This work also highlights the need for robust experimental design which includes the appropriate application of statistical procedures.