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.
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.
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.
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?"