Violin plots and bee swarm plots are better. Jittered strip plots can be okay if you're careful to avoid saturation (or more points added in the saturated region will disappear as they can't make it any darker).
Here is a great rant (borderline lecture) from Angela Collier on why they aren’t [0]
I think it's useful to be able to compare the approximate shapes of histograms during exploratory data analysis. Is the thesis of this criticism that this isn't actually a useful thing to do, or that violin plots don't achieve this, or is it "just" an aesthetic argument?
EDIT: Maybe she'd be fine with using them in an exploratory manner. She seems to mainly be complaining about using them in publications, meant for other people to consume. Also: I did not watch the entire video (:
Compared to these alternatives, violin plots are comically bad.
They look like vulvas. We're all adults, it's not a problem typically, but given that it's an aesthetic choice (noticing how half of the chart conveys the same info without this property), why? And it does come up, like if someone does make a joke about it, a room full of typically only well-meaning men will now look to her if she's comfortable with the joke and, what was okay before, now turns into a feeling of being singled out and outside the rest of the group
1) To show the distribution, in which case just the histogram arranged horizontally in the traditional fashion is far better than a violin plot with 2 copies of the histogram vertically and some extra quartile stuff tacked on, especially since lots of standard libraries to do violin plots do kde with very extreme smoothing so the distribution they show can be very misleading as to the real empirical distribution.
2) To highlight the summary statistics (quartiles and median) in which case just the boxplot is better because generally these are hard to read on a violin plot
In case #1 this is usually because the distribution differs significantly from a Gaussian in some interesting way that would make a boxplot irrelevant or misleading. (eg it is bimodal or multimodal).
In case #2 this is usually because the distribution is Gaussian (or otherwise standard) and you want to compare it with other standard distributions. You don't need all the information in the histogram and to include it all would obscure the important point(s) you're trying to make about the median and quartiles. What is considered standard is going to depend a lot on the domain, audience and subject matter. In her case, she's an astrophysicist, so if you're looking at say red shift data from some observation, other astrophysicists will know the distribution you would expect to get from that sort of observation for example.
That video is basically a summary of all the conversation attached to this article in some ways.
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.
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.
Is there a different name for the version of this that doesn't include the summary statistics on the same graph? I think seeing the distributions at different x-axis values (in my work, nearly always in a time series), but including the summary statistics is not as important and I agree that it's noisy.
You can even make 'em show histograms: https://miro.medium.com/v2/1*J3Q4JKXa9WwJHtNaXRu-kQ.jpeg