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We don’t. We keep just doing things and good things keep happening afterwards.
We don’t even know if those two facts are linked in any way.
Nearly irrelevant xkcd
At least in software we know where the linchpins are on some level.
Frequentist statistics are really… silly in a way. And this coming from someone who has to teach it. Sure, p is less than 5%, but you sampled 100,000 people-- an effect size of 0.05 would be significant at this rate. “bUt ItS sIgNiFiCaNt”… Oy.
I get very suspicious if a paper samples multiple groups and still uses p. You would use q in that case, and the fact that they didn’t suggests that nothing came up positive.
Still, in my opinion it’s generally OK if they only use the screen as a starting point and do follow-up experiments afterwards
Yeah, I used to work in a field with huge samples so significance wasn’t really all that useful. I usually just report significant coefficients and try to make clear what changes by model. For instance, if a type of curriculum showed improvements on test scores, you simply say how much and, possibly, illustrate it by saying if a person went from 50th percentile to 55th percentile.
Every field varies, though. I find it crazy how much psychologists I’ve worked with cared about r-squared. To each their own, I guess.
The fun part is that we don’t
