How Not to Visualize Your Simulation Output Data

Hybrid modeling and simulation studies combine well-defined methods from other disciplines with a simulation technique. Especially in the area of output data analysis of simulation studies, there is great potential for hybrid approaches that incorporate methods from machine learning and AI. For their successful application, the analytical capabilities of machine learning and AI must be combined with the interpretive capabilities of humans. In most cases, this connection is achieved through visualizations. As methods become more complicated, the demands on visualizations are increasing. In this paper, we conduct a data farming study and delve into the analysis of the output data. In doing so, we uncover typical errors in visualizations making the interpretation and evaluation of the data difficult or misleading. We then apply concepts of visual analytics to these visualizations and derive general guidelines to help simulation users to analyze their simulation studies and present results unambiguously and clearly.