Yeah, we come from very different fields. But I'll say this: > the probably-not-computer-literate author(s) used and miss the details hidden outside the data i

Yeah, we come from very different fields. But I'll say this: > the probably-not-computer-literate author(s) used and miss the details hidden outside the data in the explanatory text (e.g. samples were processed by throwing away all bits we couldn't identify!) that make the time spent useless. This is super common in my own field, and it's top-five indication that the paper is bad. If I can't understand how they manipulated the data, unless I have some specific other interest, the entire paper goes in the trash. There's no excuse. For example, it's pretty routine in biology and medicine to see high profile papers that invent some wild/complex "statistical pseudo-control", instead of actually doing a good experiment (which is hard, and probably why the question is unpublished). These papers are basically never worth reading. See also: not using conventional statistical tests, not including uncertainty estimates, not using standard/vanilla models before jumping to crazy stuff. Not to be too glib, but "we entered the data into SAS/Excel/R/Matlab" is like a bright, flashing warning sign that you're about to see a clown show.