Model-based design of experiments (DoE) aims at identifying the most informative experimental points in order to estimate model parameters, based on certain model assumptions. However, the parameters entering these model assumptions are generally known only within a certain accuracy. This leads to the question in how far this affects the optimal experimental designs. This report shows how the effect of uncertain model parameters can be quantified by introducing the concept of design clusters. It turns out that the sharp experimental points are smeared out to such clusters, which size depends on the uncertainty level and the design quantity. We also suggest a modification of parallel coordinate plots to visualize the clusters. The corresponding Python codes are available as well.