Re: Race, intelligence, and anti-racist prejudice (Was: Genetic Evolution)
JAMES BENTHALL (email@example.com)
2 Feb 1995 15:06 CST
In article <house.791690716@helios>, firstname.lastname@example.org.EDU.AU (ron house) writes...
>email@example.com (JAMES BENTHALL) writes:
>>In article <firstname.lastname@example.org>, email@example.com (Patrick R. Collins) writes...
>>>Frank Fujita (firstname.lastname@example.org) wrote:
>>>: JAMES BENTHALL (email@example.com) wrote:
>>>: : All I.Q. tests fail the t-test. Variation within a population is always
>>>: : greater than between populations.
>>>: I'm unfamiliar with your usage of "t-test." Could you elaborate?
>>>: Frank Fujita
>>>A t-test is a statistical test. It lets you know if the average of a sample
>>>is different from a known population when the standard deviation is not
>I don't know about the specific question at issue here, but James
>Banthall's statement is dubious from a mathematical viewpoint.
>Variations between two groups can indeed be much less than the variations
>within a group and still be statistcally significant. With billions
>of 'samples' involved (as is the case with questions about various
>populations of people), even a minute average difference can be
>Note: 'significant', statistically, means that the probability that
>the difference is random is less than a certain percentage: often
>set at 5% or 1%. The smaller the percentage at which a difference
>is significant, the less likely it is that the difference is due
>to chance alone.
For one thing, there has never been a test that I'm aware of that involved
"billions of samples." If the variation between groups is much less than
the variation within groups what enables us to say that they are *two
Part of the confusion is my misstatement in an earlier post. (I never claimed
to be above bullshit! Just ask readers of the "What is Natural" thread :-)
I said t-test but what the professor actually said was the "analysis of
variance" tests (ANOVA), but t-tests also analyze variance. Differences
_between_ means are usually swamped by the variation _within_ populations.
(Hopefully,everyone can understand this statement.)
Standardized intelligent qoutient tests are usually felled by the analysis
of variance. Differences between populations are so much greater than
differences within populations that it makes it rather difficult to say
generalizations such as "Population A tests consistently above that of
population B." As a result, folks engaged in these kind of tests don't
run ANOVA tests.
I have no references, only the reassurance of an esteemed professor of
anthropology that taught my stats class. If you want the truth you must
INVESTIGATE. I, frankly, don't have time.
BTW, for those interested in the Bell Curve controversy, the most recent
"In These Times" (Feb. 6-19) has an excellent book review of it by a
professor of human development. It seems that a certain Abecedarian Project
directly repudiates the assertions of Murray and Herrnstein, but they readily
dismiss it because of this fact. I knew Margaret was correct!