i think we can acknowledge it's unfair that attacks on academic titles / expertise will tend to target women and minorities (& that we naturally wish to defend ourselves) while also not believing that anything on this earth is or can be earned because merit is a broken concept
not here for people whose expertise is never questioned (nor time devalued and service assumed available) pontificating about how titles are sooo silly, but definitely also not here for "i earned this"
you don't "earn" a phd with hard work, you get one by being cursed by a malevolent spirit
sorry sorry i'm trying to be serious on here but who has the patience
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statistics is the most beautiful and egalitarian discipline of all of those i have studied, because when we are confused about anything in statistics we are almost always confused about the elementary, foundational concepts
in my personal experience this is not true when learning languages, history, economics, or other branches of mathematics
Example 1: sometimes very smart people suggest nonlinear regression as an alternative to quantile regression, when these address COMPLEYELY different problems. the former relaxes the linearity of the conditional mean function and the latter relaxes the focus on the mean itself.
So some very smart folks have asked about how we would apply the AMIP metric to studies of rare events. This kicked off a discussion of what robustness checks are really for, and I want to take that set of questions seriously in this thread.
I think robustness checks mainly (ought to) function to illuminate how variation in the data is being used for inference, and we should then be able to discuss whether we think this is a reasonable situation and adjust our confidence in the results.
The problem is not that there is SOME change to which our analyses are sensitive -- of course there is, they has to be. If your results aren't affected by ANY change you make to the analysis, something has gone horribly wrong with the procedure.
Guys this paper is super important. Arnold, Hull and Dobbie are among the most careful applied econometricians we have, and the explosion of algorithmic decision making means this method -- and their finding of pervasive discrimination -- could hardly be more timely.
hey since we were discussing the other day how even just "select high contrast areas to thumbnail" is a racist decision rule given the history of photography AND since you guys only understand one language, i made this for u
"average person eats 3 spiders a year" factoid actualy just statistical error. average person eats 0 spiders per year. Spiders Georg, who lives in cave & eats over 10,000 each day, is the AMIS. we've decided to show quantiles of the spider consumption distribution instead.