I'm not trying to be facetious, but isn't this something you learn in junior-level stats? I had this drilled in in both undergrad math courses and grad machine learning courses; I'm confused to see it warrant an article.
So yes, it is something you learn in introductory quantitative methods classes. But I don't think most researchers understand just how much it matters.
Also, a key R package for producing regression tables of coefficients for journal articles is called 'stargazer'. Given the unwarranted focus of many readers on those indicia of 'significant' results, I think it's well named.
I currently have the opposite problem. Given that I work with very large online datasets (N=1M or so) everything, including the random noise, is statistically significant to p<0.05. It really is effect sizes or busy at that point.
Statistics is directly necessary in ML, so it's a "profit center" and emphasized. In many sciences it's treated like a cost center (something that you need, like IT, but that lies outside of your central expertise.)
The basic problem is under the current funding environment it is far better to pump out a dozen wrong papers than one carefully researched paper.
That often works...right up until the moment when a scientist has to step outside that context.
This often cuts both ways though. I have seen beautiful math and statistics around problems that don't make any sense if you've taken more than one semester of microbiology.