4 ms·
In the first paragraph you're asking for causal explanation. Then in the second paragraph you're dishing out a bunch of potential causal explanations as confoun
by WhompingWindows 7y ago
In the first paragraph you're asking for causal explanation. Then in the second paragraph you're dishing out a bunch of potential causal explanations as confounders. Some researchers would deem it over-reaching to dish out causal explanations willy nilly. Most of those questions should be relegated to follow-up studies or a BRIEF mention in the discussion section.
It's perfectly okay science to simply observe a phenomenon. If they did try to provide causal explanations as you say, they'd then be open to being lambasted for overreaching based on shaky social science research.
- im3w1l 7y agoIf you don't have the causal explanations then you cannot generalize beyond the population you sampled. What does that mean? That the correct conclusion to draw is very narrow: Men View Their Ex-Partners More Favorably Than Women Do [in Graz, Austria, in the year 2019]
- soneca 7y agoThat's correct, still, it is ok. That's why you don't make bold, generalized claims based on one study.
- zymhan 7y agoNot every bit of research results in a simple conclusion. That doesn't mean it's irrelevant.
- moate 7y ago>>Men [in their 20s] View Their Ex-Partners More Favorably Than Women[in their 20s] Do [in Graz, Austria, in the year 2019] Fixed that for you. It's even more narrow in scope.
- vanderZwan 7y agoFor the medical equivalent, see: https://twitter.com/justsaysinmice https://twitter.com/justsaysinmice
- SolaceQuantum 7y agoI mean, isn't incremental observations how science works? We got to the theory of evolution built on finch beaks in the galapagos.
- FabHK 7y agoYet another study about WEIRD people: https://en.wikipedia.org/wiki/Psychology#WEIRD https://en.wikipedia.org/wiki/Psychology#WEIRD
- LocalH 7y agoI think for the HN headline at least, the word "may" could fit after "men".
- coldtea 7y ago>If you don't have the causal explanations then you cannot generalize beyond the population you sampled. At the individual level. You can still know that you have statistically applicable results to the whole population, without having to sample everybody -- just enough to be representative of all kinds. If you do a random enough pick, then if some people fall outside your sample ("you didn't test for people that don't like water, and the casual explanation requires being OK with water to work, so excludes them from showing the same results") they'll still be outliers statistically.