DIGE is a powerful tool for measuring changes in protein expression between samples. Here we assess the assumptions of normality and heterogeneity of variance that underlie the univariate statistical tests routinely used to detect proteins with expression changes. Furthermore, the technical variance experienced in a multigel experiment is assessed here and found to be reproducible within- and across-sample types. Utilising the technical variance measured, a power study is completed for several "typical" fold changes in expression commonly used as thresholds by researchers. Based on this study using DeCyder, guidance is given on the number of gel replicates that are needed for the experiment to have sufficient sensitivity to detect expression changes. A two-dye system based on utilising just Cy3 and Cy5 was found to be more reproducible than the three-dye system. A power and cost-benefit analysis performed here suggests that the traditional three-dye system would use fewer resources in studies where multiple samples are compared. Technical variance was shown to encompass both experimental and analytical noise and thus is dependent on the analytical software utilised. Data is provided as a resource to the community to assess alternative software and upgrades.