2 ms·

In the literature I spend the most time with (archaeology/anthropology), the abstract is often focused on the broad applicability of the research to the field,

In the literature I spend the most time with (archaeology/anthropology), the abstract is often focused on the broad applicability of the research to the field, which I either don't care about or already know (often both). The last line/paragraph of the introduction contains the specific contributions/findings of the paper. If and only if that's reasonable might it be worth investing the time to read further. I only rarely touch the data tables (assuming they even exist), because I don't want to spend an hour trying to understand whatever software nightmare 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.

timr 3y ago
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pseudo-control", instead of actually doing a good experiment (which is hard, and probably why the question is unpublished).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.
- AlotOfReading 3y ago
  Unfortunately, most of the foundational papers in the field did exactly that, usually without explicitly stating. It was pretty much the standard of practice for all bone analysis prior to ~2010 or so when microstructure analysis became a practical thing. Similar issues in other types of samples exist and it's still common today, it was just what came to my mind as the first example.

timr 3y ago

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.

AlotOfReading 3y ago
Unfortunately, most of the foundational papers in the field did exactly that, usually without explicitly stating. It was pretty much the standard of practice for all bone analysis prior to ~2010 or so when microstructure analysis became a practical thing. Similar issues in other types of samples exist and it's still common today, it was just what came to my mind as the first example.