Among participants in my workshops-who generally have above-average graphicacy levels to begin with-typically less than 20 percent already know how to read box plots.
Let me share a few observations from my experiences explaining how to read box plots to thousands of workshop participants over the years: I can read box plots just fine and so can the audiences for my charts.” “I have no idea what you’re talking about. In my experience, though, virtually all the types of insights that chart creators try to communicate using box plots can be communicated using simpler, more familiar, and, frankly, better-designed chart types that don’t require audiences to slog through a multi-minute comprehension process beforehand.
If a chart type requires that much time to explain, it had better deliver a huge “epiphany payoff,” as data storytelling expert Brent Dykes calls it, i.e., it had better deliver valuable insights that can’t be communicated using simpler, more familiar chart types that require less time and effort for the audience to understand. Why not? Well, if you watched the explanatory video linked above, you saw that it took me over four minutes to explain how to read a box plot. I used to use box plots often, but I rarely use them now. Not sure what a box plot (a.k.a., a “box-and-whisker plot”) is or how to read one? If so, you should watch this explanatory video before continuing. Alternative chart types are virtually always easier to learn how to read, more informative, or both. This has caused innumerable distribution-based insights to fail to land with audiences who weren’t willing or able to grasp them. Tl dr: After having explained how to read box plots to thousands of workshop participants, I now believe that they’re poorly conceived (box plots, not workshop participants 😉 ), which makes this classic chart type unnecessarily unintuitive, hard to grasp, and prone to misinterpretation.
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