Here's the logical structure of what you will be taught in terms of #statistics as a masters student in pretty much any #science field.
If MY DATA is a sample from two random number generators of PARTICULAR TYPE, and MY TEST has a small p value then MY FAVORITE EXPLANATION FOR THE DIFFERENCES IS TRUE.
This is, quite simply, a logical fallacy. The first thing wrong is that your data IS NOT a sample from a random number generator of that particular type. So we can ignore the rest logically.
So the very basic stuff you are taught in masters level #biostats or #socialsciences stats or whatever is built on two layers of logical fallacy.
So that's why poor stats practices are absolutely more common than good stats practices. That's why the kind of thing posted on the Reddit post I mentioned in my recent posts is actually everywhere in science. Students are literally taught to do things wrong in the textbooks.