They're the single best example of why bar charts need to die that I think I've ever encountered. Particularly in biology, where you get two bars with maybe two error bars between them and the inevitable "*" – at best, something that conveys four independent numbers, badly; at worst, actively misleading. I'm very much with Tufte on the "data-ink ratio" being a good metric for a plot: violin plots have a lot of data and surprisingly little ink.
This really depends on what you're trying to convey. I think bar charts with (mostly non-overlapping) error bars in the appropriate context can be quite useful.
One of the funniest assignments I got in university was a recent one from a finance course: The prof had us generate a bunch of random company characteristics and yearly returns, then fish for correlations and come up with fake newspaper-style stories about why these "effects" we found made sense.
Mean, variance, and linear regression lines are all the similar for the datasets that look very different when graphed.
Probably a great statistics book that covers this but it’s also in books that teach machine learning. For example, this book covers this and many other fundamentals:
https://www.autodesk.com/research/publications/same-stats-di...