As we enter an age in which genomics and bioinformatics make possible the discovery of new knowledge about the biological characteristics of an organism, it is critical that we attempt to report newly discovered "significant" phenotypes only when they are actually of significance. With the relative youth of genome-scale gene expression technologies, how to make such distinctions has yet to be better defined. We present a "mask technology" by which to filter out those levels of gene expression that fall within the noise of the experimental techniques being employed. Conversely, our technique can lend validation to significant fold differences in expression level even when the fold value may appear quite small (e.g. 1.3). Given array-organized expression level results from a pair of identical experiments, our ID Mask Tool enables the automated creation of a two-dimensional "region of insignificance" that can then be used with subsequent data analyses. Fundamentally, this should enable researchers to report on findings that are more likely to be in nature truly meaningful. Moreover, this can prevent major investments of time, energy, and biological resources into the pursuit of candidate genes that represent false positives.