Medically, the effects of a treatment on among individual variation in health have direct implications for personalized medicine. Ecologically, among-individual variation governs a species niche and is the grist of evolution by natural selection. However, experimental designs and analytical paradigms in biology are heavily focused on detecting the effects of treatments on population averages. As a result, we have a comparatively poor understanding of how environments and treatments affect among-individual variation. Over the last few years I have been developing tools for meta-analysis, which allow the user to combine the results of published studies to assess the effects of treatments on variation. These methods require only those summary statistics that are reported as a matter of standard practice, and integrate easily with commonly used meta-analytic softwares. I will present a summary of the methodology, as well as examples of application that are pertinent to research goals of the Charles Perkins Centre.