To be honest, I doubt Munroe wants to say “if the effect is smaller than you, personally, can spot in the scatterplot, disbelieve any and all conclusions drawn from the dataset”. He seems to be a bit more evenhanded than that, even though I wouldn’t be surprised if a sizable portion of his fans weren’t.
It’s kinda weird, scatterplot inspection is an extremely useful tool in principled data analysis, but spotting stuff is neither sufficient nor necessary for something to be meaningful.
But also… an R^2 of .1 corresponds to a Cohen’s d of 0.67. if this were a comparison of groups, roughly three quarters of the control group would be below the average person in the experimental group. I suspect people (including me) are just bad at intuitions about this kinda thing and like to try to feel superior or something and let loose some half-baked ideas about statistics. Which is a shame, because some of those ideas can become pretty, once fully baked.
To be honest, I doubt Munroe wants to say “if the effect is smaller than you, personally, can spot in the scatterplot, disbelieve any and all conclusions drawn from the dataset”. He seems to be a bit more evenhanded than that, even though I wouldn’t be surprised if a sizable portion of his fans weren’t.
It’s kinda weird, scatterplot inspection is an extremely useful tool in principled data analysis, but spotting stuff is neither sufficient nor necessary for something to be meaningful.
But also… an R^2 of .1 corresponds to a Cohen’s d of 0.67. if this were a comparison of groups, roughly three quarters of the control group would be below the average person in the experimental group. I suspect people (including me) are just bad at intuitions about this kinda thing and like to try to feel superior or something and let loose some half-baked ideas about statistics. Which is a shame, because some of those ideas can become pretty, once fully baked.