Hands-On Data Visualization

Chapter 14. Detect Lies and Reduce Bias

The goal of data visualization is to encode information into images that capture true and insightful stories. But we’ve warned you to watch out for people who lie with visualizations. Looking back at income inequality examples in the Introduction, we intentionally manipulated charts in Figures I-1 and I-2 and maps in Figures I-3 and I-4 to demonstrate how the same data can be rearranged to paint very different pictures of reality. Does that mean all data visualizations are equally valid? Definitely not. On closer examination, we declared that the second of the two charts about US income inequality was misleading because it intentionally used an inappropriate scale to hide the truth. We also confided that the two world maps were equally truthful, even though the US appeared in a darker color (signaling a higher level of inequality) than the other.

How can two different visualizations be equally right? Our response may conflict with those who prefer to call their work data science, a label that suggests an objective world with only one right answer. Instead, we argue that data visualization is best understood as an interpretative skill that still depends on evidence, but in which more than one portrayal of reality may be valid. As you recall, our field has only a few definitive rules about how not to visualize data, which we introduced in “Chart Design Principles” and “Map Design Principles”. Rather than a binary world, we argue that visualizations ...