Women who wore an obese body suit, which creates the psychosocial experience of feeling overweight, consumed more snack foods. https://www.sciencedirect.com/science/article/pii/S0195666317313041 …pic.twitter.com/tGQVSJxwLo
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Women who wore an obese body suit, which creates the psychosocial experience of feeling overweight, consumed more snack foods. https://www.sciencedirect.com/science/article/pii/S0195666317313041 …pic.twitter.com/tGQVSJxwLo
The p values for women eating more when wearing a body suit in the two experiments were .029 and .046 (or 0.032). Except for ridiculing them, there's no reason to give attention to these silly studies, Rolf.
Wouldn't these points make more sense to the peer reviewers and the editors of the academic journal that published that stuff in the first place?
If my points, or the replication crisis in general, made sense to them, they wouldn't publish this stuff.
OK, then thanks for pointing out. I feel a bit hard pressed to do the job of those who are supposed to do the job for me.
I'm confused as to what you think your job is. Surely this kind of basic quality checking falls under the purview of a science writer?
I am processing and posting incredible amounts of research on a daily basis. I do critically consult the methods department. But I can't puzzle over every statistic, the job of high impact peer review journals. But I applaud critical comments, that is post peer review.
If science writers adopted certain simple heuristics, I think the quality of their output would greatly improve. For example, if an experiment has fewer than 100 people per condition, don't write about, FORGET IT.
Why should I be subjected to harsher criteria than the high impact journals? If I follow your reasoning, I can stop reporting about all cognitive neuroscience, and most other stuff.
You should stop reporting about most neuroscience because it is shit tier science. Only report high N studies for that field, preferably >200. There's a lot of flexibility in their studies, so set the N bar higher.https://www.nature.com/articles/nrn3475 …
Also good to check whether your 200 subjects are a fair representation of the general population.
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