Personalized Evidence-Based Medicine (EBM) aims to estimate patient specific causal effects using covariate information. In order to adequately estimate these Individual Treatment Effects (ITEs), a thorough understanding of the role of covariates in heterogeneous datasets is necessary. In this preliminary work, we distinguish prognostic factors that influence the outcome variable, from effect modifiers, which influence the treatment effect. By means of a small synthetic data experiment where we temporarily disregard the fundamental problem of causal inference, we evaluate within-subjects variance for three possible distributions of ITEs, while keeping the Average Treatment Effect (ATE) fixed. The hypothetical nature of the experiment allows us to further understand the role of prognostic factors and effect modifiers in estimating ATEs and ITEs.