Epigenetic modifications of DNA, such as cytosine methylation are differentially abundant in diseases such as cancer. A goal for clinical research is finding sites that are differentially methylated between groups of samples to act as potential biomarkers for disease outcome. However, clinical samples are often limited in availability, represent a heterogeneous collection of cells or are of uncertain clinical class. Array-based methods for identification of methylation provide a cost-effective method to survey a proportion of the methylome at single base resolution. The Illumina Infinium array has become a popular and reliable high throughput method in this field and are proving useful in the identification of biomarkers for disease. Here, we compare a commonly used statistical test with a new intuitive and flexible computational approach to quickly detect differentially methylated sites. The method rapidly identifies and ranks candidate lists with greatest inter-group variability whilst controlling for intra-group variability. Intuitive and biologically relevant filters can be imposed to quickly identify sites and genes of interest.