All four box and whisker segments contain the same number of values, however, and all four segments are equally important (unless the chart author wants to draw attention to the middle two segments for a specific reason). The ‘box’ segments in each box-and-whisker shape, then, look like they represent more of something than the ‘whisker’ segments, leading to the faulty interpretation that they contain more values, or perhaps have more importance. If you’re a human with normal vision, you intuitively perceive thick, wide shapes as representing greater quantities (of whatever) than thin, narrow shapes.Let’s have a closer look at each of these problems in turn: The design of traditional box plots doesn’t make ‘visual sense.’īox plots are needlessly hard to grasp because their visual design doesn’t align well with how the human visual system works, i.e., they don’t make ‘visual sense.’ Specifically, I see three problems with the visual design of traditional box plots: Box plots conceal information that’s usually crucial to see.Box plots require audiences to grasp complex concepts that they don’t need to understand in most cases.The design of traditional box plots doesn’t make ‘visual sense,’ which makes them unnecessarily hard to learn how to read and prone to misinterpretation.What, exactly, are those flaws? I see three significant ones, which I’ll examine in detail: These issues arise from what I see as flaws in the traditional box plot design, which was first proposed by Mary Spear in 1952 and refined by dataviz legend John Tukey in 1969 (I can feel the flames licking at my heels already).
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