Tufte’s primary argument in this chapter is to “add detail to clarify” for many types of data complexity. At first, this seemed to go against what we had read about chartjunk and designing to capture the essentials, but it was clarified for me when he wrote “Clutter and confusion are failures of design, not attributes of information.” I think the examples he examines lend themselves particularly well to this paradigm, but I don’t know if it’s always generalizable. For the maps he examines, because maps are fully continuous and the scale can be decided on, the scale and data labeled on it can always be chosen to capture as much data as possible without being overwhelming or cluttered, and we see this executed really well in the Tokyo mesh maps. This technique also works really well when the data points cannot be summed up or summarized, such as with the Vietnam Veterans Memorial, where every name is a full life that was lost, so simply laying out every single one communicates scale without trivializing any soldier. While all these examples use the principle well, I worry that the advice is misleading because often it is hard to create a data-rich visualization while also communicating a specific point. If the data is just being published for wide use and can allow interactivity, detail is incredibly useful, but often data visualizations are made to communicate a specific idea which can be lost in high levels of detail. Still, this point is important to keep in mind to ward off instances of over=simplification, and make visualizations as dense as makes sense for every given story.