Y'all picked a funny time to nitpick at standard academic boilerplate. If we discounted all research that only "associated" things, then we wouldn't know much at all! Then again, arguably we don't.
I wouldn't call this a minor detail (i.e., nitpicking), and it is worth pointing out again and again when these studies get public attention.
We should encourage stronger research designs (including A/B tests) if we care about the impact of AI use on mental health outcomes. A study like this one cannot say anything about the effect at all (it is even possible that AI use will have a positive impact on mental health).
The translation between academic boilerplate and its real-world meaning and ramifications should be much more widely known. I wish more people had been nitpicking such things around 6 years ago.
As for this research particular...pfff...I'm rooting for the collapse of this LLM-fuelled craze, so I'm biased.
The "correlation is not causation" argument gets brought up every single time such a study is shared on HN, so I'm not sure what you mean by "picked a funny time"?
Anyway there's no reason to discount it, but it does mean you can't run with the assumption that there is causation.
I don't think psychology is useless, not one bit. But specifically the way modern papers publish findings make me distrust basically all statistical studies in the social sciences, aside from even the most basic philosophical issues that arise from these kinds of studies (people are very different, etc.).
Like even if you accept a bunch of premises to make the studies even work, the raw stats are often so bad and there's no rigor to try and actually explain alternatives that I just have stopped reading them entirely.
Again, I'm not one to hate on the social sciences. History, anthropology, politics, law, psychology, sociology, all of that is very interesting and important. But the horrible statistics that don't understand garbage in garbage out have turned me off of it. Much rather read qualitative studies that actually try to gather detailed, real data, even if it's not as automated as a random survey
erikgahner|2 days ago
We should encourage stronger research designs (including A/B tests) if we care about the impact of AI use on mental health outcomes. A study like this one cannot say anything about the effect at all (it is even possible that AI use will have a positive impact on mental health).
nDRDY|2 days ago
As for this research particular...pfff...I'm rooting for the collapse of this LLM-fuelled craze, so I'm biased.
unknown|2 days ago
[deleted]
squigz|2 days ago
Anyway there's no reason to discount it, but it does mean you can't run with the assumption that there is causation.
sigbottle|2 days ago
Like even if you accept a bunch of premises to make the studies even work, the raw stats are often so bad and there's no rigor to try and actually explain alternatives that I just have stopped reading them entirely.
Again, I'm not one to hate on the social sciences. History, anthropology, politics, law, psychology, sociology, all of that is very interesting and important. But the horrible statistics that don't understand garbage in garbage out have turned me off of it. Much rather read qualitative studies that actually try to gather detailed, real data, even if it's not as automated as a random survey