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Eugine_Nier comments on Rationality Quotes November 2013 - Less Wrong
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West Hunter
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What's wrong here? 4 degrees of accuracy for brain size and no error bars? That's a sign of someone being either intentionally or unintentionally dishonest.
Quick Googling shows that there's a paper published that states that European's average cranial capacities is 1347.
Rather then describing the facts as they are he paints things as more certain than they are. I think that people who do that in an area, where false beliefs lead to people being descrimited, are in no position to complain when they some social scorn.
How meaningful are figures on brain size without figures on overall body size?
Well, he did say “about”.
That's close enough to not effect his point, or even the order. I think you're engaging in motivated continuing to avoid having to acknowledge conclusions you find uncomfortable.
Do you also apply the same criticism to the (much larger number of) people how make (much larger errors) in the direction of no difference? Also, could you taboo what you mean by "descrimited". Steelmanning suggests you mean "judged according to inaccurate priors", yet you also seem be implying that inaccurately equaliterian priors aren't a problem.
Whatever the problem with non-factually-based equality may be, it is not a problem of discrimination, so the same criticism does not apply.
This gets back to the issue that neither you nor Christian have defined what you mean by "discrimination". I gave one definition: "judged according to inaccurate priors", according to which your comment is false. If you want to use some other definition, please state it.
Why would you think we are not using it in the standard sense? "Discrimination is the prejudicial and/or distinguishing treatment of an individual based on their actual or perceived membership in a certain group or category"
By that reasoning, refusing to hire someone who doesn't have good recommendations, is discrimination, because you're giving him distinguishing treatment (refusing to hire him) based on membership in a category (people who lack good recommendations).
I think you have some assumptions that you need to make explicit, after thinking them through first. (For instance, one obvious change is to replace "category" with "irrelevant category", but that won't work.)
Oh dear. Whoever wrote the WP article I was quoting didn't steelman their definition.
Wikipedia is supposed to use what's in the sources. They're not allowed to steelman.
It may just mean "group or category which I like", but I wouldn't count that as steelmanning.
The best I can come up with is "group or category which has, in the past, often been subject to inaccurately negative judgment based on inaccurate priors". In fact, let's try that one.
Well, the recommendations you have are to some extent the result of your choices and actions, but whether your name sounds black hardly is. So, regret-of-rationality considerations apply more to the former than to the latter.
So you are saying that I should modify the definition of "discrimination" supplied by TAG, to include a qualifier "only as a result of your choices and actions"?
That seems to say that some forms of religious discrimination don't count (choosing not to convert to Christianity is a result of your own choices and actions). It also ignores the fact that it is possible for someone to fall into a group where some of the group's members got there by their own choices and actions but some don't--not every person who can't get good recommendations is in that situation because of his own choices and actions. In fact, there's a continuum; what if, say, 10% of the people in a category got there by their own choices and actions but the other 90% had no choice?
Yes, it's a continuum. That's why I said “to some extent”, “hardly”, and “more ... than”.
So would you oppose discrimination against wheelchair bound construction workers?
First sorry for the typo.
Claiming 4 degrees of accuracy means, claiming that the factor of uncertainity about the difference is off by a factor of more than ten.
Understanding the uncertainity that exist in vital for reasoning effectively about what's true.
Different people have different goals. If your goal is the search for truth than it matters greatly whether what you speaking is true.
If your goal is to spread memes that produce social change than it makes sense to use different criteria.
What does discrimination mean? If a job application with a name that common with black people gets rejected while an identical one with a name that's common with white people gets accepted that would be an example of bad discrimination.
Does it matter if having said name is in fact correlated with job performance?
Only if it's still correlated when you control for anything else on the CV and cover letter, incl. the fact that the candidate is not currently employed by anyone else.
Being correlated isn't very valuable in itself. Even if you do believe that blacks on average have a lower IQ, scores on standardized test tell you a lot more about someone IQ.
The question would be whether the name is a better predictor of job performance than grades to distinguish people in the population of people who apply or whether the information that comes from the names adds additional predictive value.
But even if various proxies of social status would perform as predictors I still value high social mobility. Policies that increase it might not be in the interest of the particular employeer but of interest to society as a whole.
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.
To the best of my knowledge, no studies have been done which submit applications where the obviously black names have higher qualifications in an attempt to determine how many GPA points an obviously black name costs an applicant. (Such an experiment seems much more difficult to carry out, and doesn't have the same media appeal.)
So, this "only question" formulation is a little awkward and I'm not really sure what it means. For my part I endorse correctly using (grades + name) as evidence, and I doubt that doing so is at all common when it comes to socially marked names... that is, I expect that most people evaluate each source of information in isolation, failing to consider to what extent they actually overlap (aka, screen one another off).
ChristianKI brought up the proposition "(name)>(grades)" where > means that the prediction accuracy is higher, but the truth or falsity of that proposition is irrelevant to whether or not it's epistemically legitimate to include name in a decision, which is determined by "(name+grades)>(grades)".
Doing things correctly is, in general, uncommon. But the shift implied by moving from 'current' to 'correct' is not always obvious. For example, both nonsmokers and smokers overestimate the health costs of smoking, which suggests that if their estimates became more accurate, we might see more smokers, not less. It's possible that hiring departments are actually less biased against people with obviously black names than they should be.
It's even possible that if the costs of smoking are overestimated, more people should be smoking-- part of the campaign against smoking is to underestimate the pleasures and social benefits of smoking.
...insofar as their current and future estimates of health costs are well calibrated with their actual smoking behavior, at least. Sure.
Well, it's odd to use "bias" to describe using observations as evidence in ways that reliably allow more accurate predictions, but leaving the language aside, yes, I agree that it's possible that hiring departments are not weighting names as much as they should be for maximum accuracy in isolation... in other words, that names are more reliable evidence than they are given credit for being.
That said, if I'm right that there is a significant overlap between the actual information provided by grades and by names, then evaluating each source of information in isolation without considering the overlap is nevertheless a significant error.
Now, it might be that the evidential weight of names is so great that the error due to not granting it enough weight overshadows the error due to double-counting, and it may be that the signs are such that double-counting leads to more accurate results than not double-couting. Here again, I agree that this is possible.
But even if that's true, continuing to erroneously double-count in the hopes that our errors keep cancelling each other out isn't as reliable a long-term strategy as starting to correctly use all the evidence we have.
That in no way implies that it would be a good choice for people to smoke more. People don't make those decisions through rational analysis.
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.
Source is here. SD for Asians and Europeans is 35, SD for Africans was 85. N=20,000.
...no? Why in the world would he present error bars? The numbers are in line with other studies, without massive uncertainty, and irrelevant to his actual, stated and quoted, point.
His stated point is about telling things that everybody is supposed to know.
If you have an SD of 35 for an average of 1362 you have no idea about whether the last digit should be a 2. That means either you do state an error interval or you round to 1360.
Human height changed quite a bit over the last century. http://www.voxeu.org/article/reaching-new-heights-how-have-europeans-grown-so-tall . Taking data about human brainsize with 4 digit accuracy and assuming that it hasn't changed over the last 30 years is wrong.
European gained a lot of bodymass over the last 100 years due to better nutrition. The claim that it's static at 4 digit in a way where you could use 30 year old data to describes todays situation, gives the impression that human brainsize is something with is relatively fixed.
The difference in brain size between Africans and European in brainsize in that study is roughly the difference in height between todays Europeans and Europeans 100 years ago.
Given that background taking a three decades old average from one sample population and claiming that it's with 4 digits accuracy the average that exist today is wrong.
If individual datapoints have an SD of 35, and you have 20000 datapoints, then the SD of studies like this is 35/sqrt(20000)≈0.24. So giving a one's digit for the average is perfectly reasonable.
According to the paper the total mean brain size for males is 1,427 while for females it's 1,272. Given around half women and half men the SD per point should be higher than 35.
(Assuming the sample is unbiased.)
No, that was absolutely not his point. I don't understand how you could have come away thinking that- literally the entire next paragraph directly stated the exact opposite:
More generally, that was not a tightly reasoned book/paper about brainsize. That line was a throwaway point in support of a minor example ("For example, average brain size is not the same in all human populations") on a short blog post. Arguments about the number of significant figures presented, when you don't even disagree about the overall example or the conclusion, are about as good an example of bad disagreement as I can imagine.
I don't think that the following classes are the same:
(1) Facts everyone should know.
(2) Facts everyone knows.
I think the author claims that this is a (1) fact but not a (2) fact.
His claim was:
(a) Everybody knew that different ethnicities had different brain sizes (b) It was an uncomfortable fact, so nobody talked about it (c) Now nobody knows that different ethnicities have different brain sizes