On the other hand, I think a lot of these "bad graphs" are very intentionally chosen precisely in order to hide the small number of data points, or an underlying distribution that looks suspicious, etc.
So it's not so much "friends don't let friends", but more "when you see a graph that chooses to obfuscate rather than clarify, suspect it might be intentional".
Not that you're wrong but researchers are also deeply imperfect. They're rushed, they're given no time to actually improve their work, and the emphasis is entirely on 'good-enough' publications. The number of times I've been involved in a paper where the mentality wasn't "get it out the door, now" is.... zero times.
Plots often fail to clarify for the precise reason that clarification takes time and effort and those things are lacking in academia in spades. Are people intentionally hiding ugly details, definitely on occasion? But I don't think it's the primary source of such bad figures.
All that you said with a heavy dash of good data visualisation is more of a skill and an artform than many people realise.
I've had four decades of crunching numbers in a variety of Engineering, Geophysics, and science applications with a hefty amount of public consulting on a variety of applications and of the large population of those good at gathering and recording data perhaps only 20% had that extra talent for good visualisation to convey meaning without distortion.
There is a book called. "how to lie with statistics" by Huff that should probably be required reading for everyone. It's not very technical and a pretty quick read
I really enjoyed Storytelling with Data: A Data Visualization Guide for Business Professionals
by Cole Nussbaumer Knaflic. The author really breaks down the individual elements of good/bad visualizations using case studies with lots of actionable advice. Highly recommended.
Any of their performance graphs… none of them include axis labels, so you never really know what they are comparing. All you can see is that the newer chip is “faster” or “more efficient” than something else. You just never know by how much.
It definitely happens intentionally and unintentionally in psych literature, certainly the first one. Psych articles report p values over ANOVAs, and they blithely assume everything is normally distributed. For one group, there's absolutely no need to poke the sleeping dog. The other group simply has no idea. They shouldn't be doing research, but their training is lacking, and cheap PhDs aren't that easy to come by, so here we are.
I for the life of me can not produce good graphs in Matlab... I use it so rarely that I forget all the syntax and when to us hold etc. So now I just export the data and plot it with python to annoyance of my pms...
On the other hand, I think a lot of these "bad graphs" are very intentionally chosen precisely in order to hide the small number of data points, or an underlying distribution that looks suspicious, etc.
So it's not so much "friends don't let friends", but more "when you see a graph that chooses to obfuscate rather than clarify, suspect it might be intentional".