On an IQ scale where the current white American mean is set at 100 (SD = 15), the distribution for Americans as a whole has now fallen to about 96 (SD = 15.8). Now imagine a racist psychologist who was too lazy to score anyone’s IQ test results. She simply looked at the person’s face and assigned them the average score of their race:
Black Americans: IQ 85
Native Americans: IQ 86
Hispanic Americans: IQ 89
Southeast Asian Americans: IQ 92
White Americans: 100
East Asian Americans: 104
Indian Americans: 110
Jewish Americans: 110
When she calculated the average score of her sample, it clocked in at 96, which is now the U.S. average. She was very pleased with herself. Her scoring method worked!
However when her boss checked her work he was not pleased. He said that while the mean score of her sample looked good, the standard deviation was only 6.6. How can that be he asked. All the other samples of American IQ were showing a standard deviation of 15.8.
Standard deviation is the standard amount by which people vary from the mean. It is calculated by taking the average squared deviation from the mean. If the SD is large, it means there’s a lot of variability in the sample. If the SD is small, it means everyone is clustered around the average.
The reason the racist psychologist got such a small SD is that by assigning everyone the average IQ of their race, she eliminated all the IQ variability that exists within races, leaving only the IQ variability that exists between races.
So can she get the SD up to 15.8 while maintaining the perfect correlation between IQ and race? By simply converting all her IQs in her sample into Z scores:
Z score = (obtained score – mean)/SD
Z score = (obtained score – 96)/6.6
And then converting the Z scores into new IQ scores with mean of 96 and an SD of 15.8:
New IQ score = Z score(15.8) + 96
With this conversion, the IQs of every member of each race were as follows:
Black Americans: IQ 70
Native Americans: IQ 72
Hispanic Americans: IQ 79
Southeast Asian Americans: IQ 86
White Americans: 106
East Asian Americans: 115
Indian Americans: 130
Jewish Americans: 130
The racist psychologist began to laugh with evil glee. For now her sample had as much IQ variability as every other, but the correlation between IQ and race remained perfect. She had simply transferred the within race IQ variation she had removed into between race IQ variation.
Pumpkin Person 
And the point of this story is…?
op is a faggot
There is no point.
That’s the point.
Nice background change!
The only point I get out of this is that IQ differences between races are smaller than IQ differences within races.
Nice background change!
Yes, this theme is so much classier.
Besides IQ, I believe certain groups and individuals are more predisposed to what people call fast track success, and then taper off into a low social status state of crime, drugs and other degenerate things. Late bloomers mature slower and usually lead a better, more well rounded life, with big success later in life, when they hone in with all the things they have acquired.
Just look at blacks, who appear very smart during their childhood, and then gradually lose out completely to Whites and East Asians in the school setting. Low IQ blacks mature the fastest, where as high IQ East Asians mature the slowest. Notice many East Asians tend to be pedomorphic. However, it appears that Whites are more successful as late bloomers than East Asians.
I think this might shine some clues as to why many early bloomers lose out.
http://well.blogs.nytimes.com/2014/06/23/cool-at-13-adrift-at-23/?mabReward=A5&moduleDetail=recommendations-2&action=click&contentCollection=Books®ion=Footer&module=WhatsNext&version=WhatsNext&contentID=WhatsNext&src=recg&pgtype=article
Interesting link. It all fits in quite perfectly with Rushton’s r/K evolutionary theory. More r selected individuals, races and social classes are genetically predisposed to have a faster life cycle, becoming sexually active at a younger age with more numerous partners, and engage in more reckless behavior. These are the cool kids who are popular in school, but peak too soon.
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There are flaws in your math. Taking an average of an average is not a linear transformation, so you can’t do what you are attempting. Further to that by normalizing already normalized data points, you lose accuracy.
Easiest way to see that you are wrong is to note that there is x% chance of a person falling 1 SD from the original mean for their race, and a y% chance of them falling 1 SD from the new race adjusted mean. x does not equal y, meaning that your original data points will not fit the new normal distribution (you will get a much much wider tail)… so the new data which has been altered is not normally distributed (not sure why you assumed it was but you’re wrong). I.e. you will have over 2.4% of observed data points outside the 96 percentile, and the tails won’t be uniform (more of these outliers will have higher than predicted IQs.
What you should of done is fit the data after normalized it and coming up with the new IQ distribution to a model, and then used that. You should end up back where you started. You can’t manipulate away observed deviations within races. Perhaps if you were a different race you would have noticed this :p.
Race is not a continuous normally distributed variable, so it’s never going to work perfectly, but my method seems to work quite well
. On a racist IQ test, all 13% of black Americans would be assigned the lowest 13% of IQ scores. The median score in the bottom 13% is by definition the bottom 6.5%
In a normal distribution, the bottom 6.5% is 1.53 SD below the mean.
Now if the U.S. IQ distribution has a mean of about 96 and an SD of about 15.8, (which might be a bit off, but what I assumed in this post), and one wants the racist IQ test to give the same distribution of IQs as a real IQ test, then for blacks to be 1.53 SD below the mean (on a racist IQ test, not a real one) should imply an IQ of:
96 – 1.53(15.8) = 72.
This is remarkably close to the 70 I found using my method.
”Race is not a continuous normally distributed variable”
No*** why not*
Wouldn’t really be Gaussian….ADd hence not quite as useful.
*and*