Emphasis mine. I don’t think this is the question at all, because you also have the grade information; the only question is if grades screen off evidence from names, which is your second option. It seems to me that the odds that the name provides no additional information are very low.
If you combine a low noise signal with a high noise signal the combined signal can be of medium noise. Combining information isn’t always useful if you want to use both signal as proxy for the same thing.
For combining information in such a way you would have to believe that the average black with a IQ of 120 will get a higher GPA score than the average white person of the same IQ.
I think there little reason to believe that’s true.
Without actually running a factor analysis on the outcomes of hiring decision it will be very difficult to know in which direction it would correct the decision.
Even if you do run factor analysis integrating addtional variables costs you degrees of freedom so it not always a good choice to integrate as much variables as possible in your model. Simple models often outperform more complicated ones.
Human’s are also not good at combining multiple sources of information.
If you combine a low noise signal with a high noise signal the combined signal can be of medium noise. Combining information isn’t always useful if you want to use both signal as proxy for the same thing.
Agreed that if you have P(A|B) and P(A|C), then you don’t have enough to get P(A|BC).
But if you have the right objects and they’re well-calibrated, then adding in a new measurement always improves your estimate. (You might not be sure that they’re well-calibrated, in which case it might make sense to not include them, and that can obviously include trying to estimate P(A|BC) from P(A|C) and P(A|B).)
For combining information in such a way you would have to believe that the average black with a IQ of 120 will get a higher GPA score than the average white person of the same IQ.
Not quite. Regression to the mean implies that you should apply shrinkage which is as specific as possible, but this shrinkage should obviously be applied to all applicants. (Regressing black scores to the mean, and not regressing white scores, for example, is obviously epistemic malfeasance, but regressing black scores to the black mean and white scores to the white mean makes sense, even if the IQ-grades relationship is the same for blacks and whites.)
It could also be that the GPA-job performance link is different for whites and blacks, even if the IQ-GPA link is the same for whites and blacks. (And, of course, race could impact job performance directly, but it seems likely the effects should be indirect for almost all jobs.)
I think there little reason to believe that’s true.
If you’re just comparing GPAs, rather than GPAs weighted by course difficulty, there could be a systematic difference in the difficulty of classes that applicants take by race. I’ve had a hard time getting numerical data on this, for obvious reasons, but there are rumors that some institutions may have a grade bias in favor of blacks. (Obviously, you can’t fit a parameter to a rumor, but this is reason to not discount an effect that you do see in your data.)
Simple models often outperform more complicated ones.
Yes, but… motivated cognition alert. If you’re building models correctly, you take this into account by default, and so there’s no point in bringing it up for any particular input because you should already be checking it for every input.
For combining information in such a way you would have to believe that the average black with a IQ of 120 will get a higher GPA score than the average white person.
I think there little reason to believe that’s true.
Could you explain your reasoning here?
IQ is a strong predictor of academic performance, and a 1.5 sd gap is a fairly significant difference. The only thing I could think of to counterbalance it so that the average white would get a higher GPA would be through fairly severe racial biases in grading policies in their favor, which seems at odds with the legally-enforced racial biases in admissions / graduation operating in the opposite direction. Not to mention that black African immigrants, legal ones anyway, seem to be the prototype of high-IQ blacks who outperform average whites.
I am a little puzzled by the claim, which leads me to believe I’ve misunderstood you somehow or overlooked something fairly important.
If you combine a low noise signal with a high noise signal the combined signal can be of medium noise. Combining information isn’t always useful if you want to use both signal as proxy for the same thing.
For combining information in such a way you would have to believe that the average black with a IQ of 120 will get a higher GPA score than the average white person of the same IQ.
I think there little reason to believe that’s true.
Without actually running a factor analysis on the outcomes of hiring decision it will be very difficult to know in which direction it would correct the decision.
Even if you do run factor analysis integrating addtional variables costs you degrees of freedom so it not always a good choice to integrate as much variables as possible in your model. Simple models often outperform more complicated ones.
Human’s are also not good at combining multiple sources of information.
Agreed that if you have P(A|B) and P(A|C), then you don’t have enough to get P(A|BC).
But if you have the right objects and they’re well-calibrated, then adding in a new measurement always improves your estimate. (You might not be sure that they’re well-calibrated, in which case it might make sense to not include them, and that can obviously include trying to estimate P(A|BC) from P(A|C) and P(A|B).)
Not quite. Regression to the mean implies that you should apply shrinkage which is as specific as possible, but this shrinkage should obviously be applied to all applicants. (Regressing black scores to the mean, and not regressing white scores, for example, is obviously epistemic malfeasance, but regressing black scores to the black mean and white scores to the white mean makes sense, even if the IQ-grades relationship is the same for blacks and whites.)
It could also be that the GPA-job performance link is different for whites and blacks, even if the IQ-GPA link is the same for whites and blacks. (And, of course, race could impact job performance directly, but it seems likely the effects should be indirect for almost all jobs.)
If you’re just comparing GPAs, rather than GPAs weighted by course difficulty, there could be a systematic difference in the difficulty of classes that applicants take by race. I’ve had a hard time getting numerical data on this, for obvious reasons, but there are rumors that some institutions may have a grade bias in favor of blacks. (Obviously, you can’t fit a parameter to a rumor, but this is reason to not discount an effect that you do see in your data.)
Yes, but… motivated cognition alert. If you’re building models correctly, you take this into account by default, and so there’s no point in bringing it up for any particular input because you should already be checking it for every input.
Could you explain your reasoning here?
IQ is a strong predictor of academic performance, and a 1.5 sd gap is a fairly significant difference. The only thing I could think of to counterbalance it so that the average white would get a higher GPA would be through fairly severe racial biases in grading policies in their favor, which seems at odds with the legally-enforced racial biases in admissions / graduation operating in the opposite direction. Not to mention that black African immigrants, legal ones anyway, seem to be the prototype of high-IQ blacks who outperform average whites.
I am a little puzzled by the claim, which leads me to believe I’ve misunderstood you somehow or overlooked something fairly important.
I missed the qualification of speaking of whites with the same IQ. I added it via an edit.
Right, okay. I did misunderstand you. I’ll correct my comment as soon as I figure out the strikethrough function here.
I believe the primary way to get strikethrough is to strikethrough the entire comment, by retracting it.
You can use unicode.
I’d recommend Vaniver’s solution instead—IME Android phones don’t like yours.