In transcriptome profiling experiments using DNA microarrays, it is critical to maximize putatively true data discovery while keeping the false discovery rate at acceptable levels. Using previously published and verified transcriptome datasets of mice with genetically altered PS1 physiology, we present a simple, robust, and system-specific assessment of type I and type II errors in two independent microarray experimental series. We provide evidence to suggest that for maximizing true discovery and minimizing false discovery, statistical criteria alone are inferior to statistical significance plus magnitude of change criteria. Furthermore, we found that, regardless of the exact criteria used for determining differential expression, different data extraction protocols give rise to different discovery and false discovery rates. In addition, a large proportion of expression differences were both dataset and analytical approach dependent. The data assessment methods presented and discussed in this manuscript can be easily carried out on any microarray dataset using basic spreadsheet functions as the only tool needed. Finally, we provide an in-depth analysis of the hippocampal transcriptome of DeltaE9 hPS1 transgenic mice and mice with a conditional ablation of the PS1 gene.