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.

Well, I think it's crazy to let idiots keep people from using things that are useful. If it's not useful, then ok, but if it is, then that's a bad reason to avoid it.

And I just don't relate to this at all:

> you usually want to present either the distribution (in which case a horizontal histogram without extreme kde smoothing or quartile info is usually better)

Where I almost always see this is in time series plots where there is a distribution at each point. Horizontal histograms are not as intuitive for visualizing this, because plotting time on the x-axis is so universal. And while it is true that box plots work well for this when the distribution at each point is close to normal, it is not true that all data looks like this, and it's easy to not notice this if you default to using a box plot.

I do agree with this:

> or you want to highlight just the summary stats in which case the boxplot on its own (or just a table) is generally better

Yes, but you can also just leave off the summary stats from the "violin plot" (just like, as you point out, histograms usually don't and shouldn't include summary stats) in order to visualize only the shape of each distribution.

I also really don't care about the flourish of vertically centering / "reflecting" the distribution, a series of vertical histograms totally expresses the same information that I'm saying is useful here! People seem to find that ugly, which I figure is why they started doing the reflection thing to make it prettier, but I really don't have a strong view either way on which of these presentations is or isn't ugly or leads to awkward jokes. I just think "a series of distribution shapes laid out vertically" is a commonly useful visualization.

And I really don't know about your last point; I don't spend much time working with non-experts who don't understand histograms really well.