I've stopped using box plots (2021)

Presumably you're referring to the "it looks like a vulva" thing that some other commenter mentioned, which honestly makes me think I must be trying to give credence to the opinions of people who have not progressed past adolescence, if this is truly their issue.

As the author of the original Nightingale article that kicked off this (wild) thread, maybe I can clarify a few things:

My fundamental concern with box plots is that no one has ever shown me a single scenario in which a given insight was clearer in a box plot than it would be in a simpler chart type (i.e., strip plot, distribution heatmap, or stacked histograms). If someone can show me even a hand-crafted, cherry-picked scenario with the same data shown as a (well-designed) box plot AND a strip plot, distribution heatmap and stacked histograms, and in which a potentially useful insight is clearer in the box plot than in the other chart types, I’ll happily change my opinion. I’m still waiting for someone to show me such a scenario, though.

In the meantime, I’m not sure why one would use box plots when simpler chart types are available that say the same thing about the data or, in many cases, say more about the data (show gaps, multi-modal distributions, etc.). Even if the audience is very used to reading box plots, they’ll still find strip plots, distribution heatmaps and stacked histograms to be simpler to read (and will actually see gaps, clusters, etc.)

How do I know that other distribution chart types are simpler to read than box plots? Because I’ve taught these chart types to literally thousands of people of all skill levels all over the world. Quartiles are just inherently less intuitive than bins or, in the case of strip plots, no delimiters to understand at all.

Like I said, if someone can show me a scenario like the one that I described above, though, I’ll happily change my mind…

Before people jump all over me, I should clarify what I mean by a “potentially useful insight.” For example, “showing the interquartile range” is not an “insight” in this context, it’s an “observation” because it doesn’t point to any kind of action or conclusion, in and of itself. A potentially useful insight would be something like, “The employee salaries in Company A are generally higher than those in Company B.” or “Most people make close to $80K in Company A, but the salaries are much more spread out in Company B.” Basically, an “insight” in this context is a piece of information that would point directly to some kind of action or conclusion.

I naively assumed that ten years of teaching data visualization at NASA, Yale, Visa, the U.N., UofT, etc. would qualify me to write about something like this, but I guess not. Thanks for setting me straight :-) BTW, what terminology was I using incorrectly?

I think you're missing some nuance. She's saying this frequently leads to a situation where she (as a female scientist) is put in an uncomfortable/weird spot by a data visualisation because her colleagues/peers have (in your words) not progressed past adolescence. It seems completely unnecessary to use a data visualisation technique that leads to this issue, especially since it doesn't have any other particular benefit relative to more conventional techniques.

In any case - I don't personally use them not because of that but because of the reasons I gave[1] which she also mentions in the video - you usually want to present either the distribution (in which case a horizontal histogram without extreme kde smoothing or quartile info is usually better) or you want to highlight just the summary stats in which case the boxplot on its own (or just a table) is generally better. When I find I want to call out a given summary stat (median/mode/some quantile cutoff) on a histogram it's usually better in my view to just show the cutoff on the histogram and shade the tail (eg you frequently see hypothesis tests as a histogram with the critical region shaded and the CV1 number or whatever called out specifically).

[1] and one other which is they are even more confusing in many respects for non-experts than a boxplot so if I was to put one in a presentation or whatever I would find myself spending an undue amount of time explaining the plot rather than making whatever point I wanted to make with the plot which is never a good sign. It would be different for someone who tends to write for/present to fellow experts I imagine.