How not to lie with visualization

How data are represented visually has a powerful effect on how the structure in those data is perceived. For example, in Figure 1, four representations of an MRI scan of a human head are shown. The only difference between these images is the mapping of color to data values, yet, the four representations look very different. Furthermore, the inferences an analyst would draw from these representations would vary considerably. That is, variations in the method of representing the data can significantly influence the user's perception and interpretation of the data. How NOT to Lie with Visualization http://www.research.ibm.com/dx/proceedings/pravda/truevis.htm