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Simpson’s Paradox (2016)

forrestthewoods.com

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Re: Simpson’s Paradox (2016)

#81

Earlier quoted context omitted.

I still do not understand. How are men "favourites by 1%-2%" and women "by 10% plus"? Favourites, for what? And how did you calculate the margin of error for this study?

First each subject you compare the chance of admission. For men when they have a higher chance of admission, even in their most advantaged subject they have a higher chance of admission of 4%. Women on the other hand have a 20%. You can't say that they are equivalent in the least. In terms of error margins, a few percent is common, from experience. You could do a stats 95 confidence style calculation.

You're talking about the difference between the percentages of applicants of each sex that were admitted. I tabulate:

                  Men              Women              % Difference
    Department Applied  Admitted Applied  Admitted    Men     Women
    A          [825]    62%      108      [82%]               +20%
    B          [560]    63%      25       [68%]               +5%
    C          325      [37%]    [593]    34%         +3%
    D          [417]    33%      375      [35%]               +2%
    E          191      [28%]    [393]    24%         +4%
    F          [373]    6%       341      [7%]                +1%

So, there's a 20% difference for one department that is a clear outlier and then everything is within a couple of percentiles of difference. In fact, the average difference is higher for men (3.5) than for women (2.666) ignoring the outlier, since it's an outlier.

However, I'm really not sure that taking the difference between proportions of different wholes is meaningful. The numbers don't add up to 100, so what does the difference mean, exactly?

I don't know what "a stats 95 confidence style" is, or how it is related to a margin of error, so please do that calculation and post your results.

Re: Simpson’s Paradox (2016)

#82
post #33

Earlier quoted context omitted.

>> In ML encapsulation, shielding away of inner details often does not work. I call bs on this. It’s just that we haven’t yet invented a consistent type theory on top of ML.

“Just” This would be like saying “it’s just that we haven’t proven P!=NP” in CS. Best of luck. Meanwhile applied people will deal with the problem by model diagnostics and sensitivity analysis as has been done for decades. I can’t wait for the next AI winter to come. So tired of this handwaving by people who don’t seem to have practical experience.

Yeah, but you just reduced the parent comment to ML being the same as CS. And the parent is saying the opposite: that they differ from each other.

So meanwhile, speaking of applied knowledge... I believe you didn't even read what you're replying to.

Re: Simpson’s Paradox (2016)

#83
post #28

I'd like to say that the author has been reading The Book of Why , but it seems that he hasn't because he missed the punch line of the section on the paradox: you need a causal model to separate the two branches of the paradox. It's as easy to construct examples where the overall view is correct as it is so construct examples where the separate views are.

Post author here. Can confirm I’ve not read The Book of Why!

I’ll add it to my reading list.

Re: Simpson’s Paradox (2016)

#84
post #28

I'd like to say that the author has been reading The Book of Why , but it seems that he hasn't because he missed the punch line of the section on the paradox: you need a causal model to separate the two branches of the paradox. It's as easy to construct examples where the overall view is correct as it is so construct examples where the separate views are.

Post author here. Can confirm I’ve not read The Book of Why! I’ll add it to my reading list.

A warning: it's seriously self-congratulatory. But I don't know of anything better.
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