Data visualization blends art and science to convey stories from data via graphical representations. Considering different problems, applications, requirements, and design goals, it is challenging to combine these two components at their full force. While the art component involves creating visually appealing and easily interpreted graphics for users, the science component requires accurate representations of a large amount of input data. With a lack of the science component, visualization cannot serve its role of creating correct representations of the actual data, thus leading to wrong perception, interpretation, and decision. It might be even worse if incorrect visual representations were intentionally produced to deceive the viewers. This work-in-progress paper focuses on misinformation in graphical representations utilizing a case-based learning approach. The misleading data visualization examples are surveyed and projected onto fundamental units of visual communication, such as size, value, shape, size, and position. This work aims at helping viewers understand the root causes of the misuse, as well as provide basic principles for making more effective visualizations.