[SamuelMulder]

I haven't seen any reason to believe that being multidisciplinary makes this more likely. The whole problem is caused by the fact that most of the single-disciplinary researchers are using bad statistics.

Very few degree programs give scientists enough background in statistics and experimental rigor to prevent these problems.

[dnautics]

no doubt, the single-disciplinary researchers use bad statistics, too. My broader point is that multidisciplinary researchers are less likely to use statistics correctly, if for no other reason than that the fact that they are multidisciplinary means that they are less likely to be able to handle depth from a personality/self-habits point of view.

Perhaps that's a little bit of projection, but I don't use any but the most rudimentary statistics in my publications and I don't make claims about significance.

[SamuelMulder]

It would be interesting to look into this. My experience has been that multi-disciplinary researchers are more likely to be familiar with a broader range of research and familiar with statistical techniques across their different disciplines. Someone with a narrower perspective is more likely to get into bad habits related to their field.

I think your comment about them being less likely to be able to handle depth from a personality/self-habits point of view is really projecting and probably based on experiences with a few bad apples. My experience doesn't reflect that at all.

I feel like a lot of the problems with statistics come from overly insular journals and conferences where everyone does it wrong and nobody realizes that because they're all doing the same thing. I've seen this in some computer science fields.

[dnautics]

Also, I'm in chemistry and biology (don't get me started about biophysics, which is populated by dilettantes who couldn't hack it in physics thinking that biophysics is "physics" - it's not. It's chemistry)... So what it means to me to be multidisciplinary may be different from what it means to you.

People seriously just take standard deviations and then plug it into the formula to spit out p values. Without thinking about things like: "should the values be normally distributed? or log-normal?" "How does error propagate through this formula I'm using?" "Is the major source of error in the replicates that I'm using (versus something I might be normalizing to, like a mass measurement)?" "Am I in the linear range of my calibration standards?" "Is using these data quantitatively (versus qualitatively) an honest thing to do?"