1. What are the implications of ‘confirmation bias’ on data visualizations? If a viewer is prejudiced against a certain set of data, will they subconsciously reject the visualization? How can a graphic designer strategically make visualizations more compelling for people who disagree with what is being shown?

  2. “So well do we collaborate, Sloman and Fernbach argue, that we can hardly tell where our own understanding ends and others’ begins.”

    Does the “illusion of explanatory depth” apply to software in the same way it applies to household appliances? Would the average person claim to understand how an iPhone app “works,” or is computer science more mystical to the general public than toilets and zippers?

  3. “As a rule, strong feelings about issues do not emerge from deep understanding”

    I guess my big question after reading this article is: do people really care what statistics they’re presented with? Can a data visualization ultimately change someone’s mind? It seems like all the points this article is trying to make imply that people are fairly set in their ways, and often ignorant to the truth or at the very least to facts that contradict their own opinions. So, is it worth it to put effort into a visual that will be accepted by those who are predisposed to agree with it and rejected by those who aren’t? How good does a visualization have to be to prove all of these studies wrong?