If the large collection of microarray-specific statistical tools was applicable to the analysis of quantitative shotgun proteomics datasets, it would certainly foster an important advancement of proteomics research. Here, we analyze two large multi-dimensional protein identification technology (MudPIT) datasets – one containing 8 replicates of the soluble fraction of a yeast whole-cell lysate, one containing 9 replicates of a human immuno-precipitate – to test whether normalized spectral abundance factor (NSAF) values share substantially similar statistical properties with transcript abundance values from Affymetrix GeneChip data. First, we show similar dynamic range and distribution properties of these two types of numeric values. Next, we observe that the standard deviation (SD) of a protein’s NSAF values is dependent on the average NSAF value of the protein itself, following a power law. This relationship can be modeled by a power law global error model (PLGEM), initially developed to describe the variance-versus-mean dependence that exists in GeneChip data. PLGEM parameters obtained from NSAF datasets prove to be surprisingly similar to the typical parameters observed in GeneChip datasets. The most important common feature identified by this approach is that, although in absolute terms the SD of replicated abundance values increases as a function of increasing average abundance, the coefficient of variation – a relative measure of variability – becomes progressively smaller under the same conditions. We next show that PLGEM parameters are reasonably stable to decreasing numbers of replicates. We finally illustrate one possible application of PLGEM in the identification of differentially abundant proteins, which might potentially outperform standard statistical tests. In summary, we believe that this body of work lays the foundation for the application of microarray-specific tools in the analysis of NSAF datasets.