Nucleotide-microarray technology, which allows the simultaneous measurement of the expression of tens of thousands of genes, has become an important tool in the study of disease. In disorders such as malignancy, gene expression often undergoes broad changes of sizable magnitude, whereas in many common multifactorial diseases, such as diabetes, obesity, and atherosclerosis, the changes in gene expression are modest. In the latter circumstance, it is therefore challenging to distinguish the truly changing from non-changing genes, especially because statistical significance must be considered in the context of multiple hypothesis testing. Here, we present a balanced probability analysis (BPA), which provides the biologist with an approach to interpret results in the context of the total number of genes truly differentially expressed and false discovery and false negative rates for the list of genes reaching any significance threshold. In situations where the changes are of modest magnitude, sole consideration of the false discovery rate can result in poor power to detect genes truly differentially expressed. Concomitant analysis of the rate of truly differentially expressed genes not identified, i.e., the false negative rate, allows balancing of the two error rates and a more thorough insight into the data. To this end, we have developed a unique, model-based procedure for the estimation of false negative rates, which allows application of BPA to real data in which changes are modest.