Paleontologists without vertebrae? I'm surprised you even found 20 ;-)
----- Original Message -----
> One of my Professors used to use the Invertebrate Paleontologists as his
> example of a POPULATION. I think at that time there were less than 20
> people who were Invertebrate Paleontologists.
>
> -- Joe
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>
>
> ----- Original Message -----
> From: Robert Dawson <[EMAIL PROTECTED]>
> To: dennis roberts <[EMAIL PROTECTED]>; <[EMAIL PROTECTED]>;
<[EMAIL PROTECTED]>
> Sent: Monday, April 17, 2000 9:54 AM
> Subject: Re: hyp testing -Reply
>
>
> |
> | ----- Original Message -----
> | From: dennis roberts
> | > At 10:32 AM 4/17/00 -0300, Robert Dawson wrote:
> | >
> | > > There's a chapter in J. Utts' mostly wonderful but flawed
low-math
> | intro
> | > >text "Seeing Through Statistics", in which she does much the same.
She
> | > >presents a case study based on some of her own work in which she
looked
> | at
> | > >the question of gender discrimination in pay at her own university,
and
> | > >fails to reject the null hypothesis [no systemic difference in pay
> | between
> | > >male and female faculty]. She heads the example "Important, but not
> | > >significant, differences in salaries"; comments (_perhaps_
technically
> | > >correctly but misleadingly) that "a statistically naive reader could
> | > >conclude that there is no problem" and in closing states:
> |
> | and Dennis Roberts replied:
> |
> | > the flaw here is that ... she has population data i presume ... or
about
> | as
> | > close as one can come to it ... within the institution ... via the
budget
> | > or comptroller's office ... THE salary data are known ... so, whatever
> | > differences are found ... DEMS are it!
> | >
> | > the notion of statistical significance in this case seems IRRELEVANT
...
> | > the real issue is ... given that there are a variety of factors that
might
> | > account for such differences (numbers in ranks, time in ranks, etc.
etc.)
> | > .... is the remaining difference (if there is one) IMPORTANT TO DEAL
WITH
> | ...
> |
> |
> | If one can totally explain all contributing factors, so that a model
> | with significantly fewer parameters than there are faculty fits
everybody to
> | within a practically significant margin of error, then yes, either the
model
> | continues to work with gender removed or it doesn't.
> |
> | If, on the other hand, there are unknown sources of variation (a
> | reasonable assumption in any situation involving people), or more
sources of
> | variation than there are data (another good bet if one thought hard
enough),
> | one cannot automatically go from the observation
> |
> | (*) "The average pay of female faculty members here is less than that
of
> | male faculty members"
> |
> | to the apparently desired conclusion
> |
> | (**) "There is a gender-based _pattern_ of discrimination in faculty
> | salaries"
> |
> | without considering the study as a pseudo-experiment, and analyzing it
as
> | such. One would be trying to decide: is the difference between mean
male
> | and female faculty salaries greater than one would expect if one took N1
> | males and N2 females and assigned factors such as experience, rank,
> | skill/luck at negotiating a first contract, demand for specialties,
merit
> | pay actually deserved [as opposed to given on a gender basis], etc. at
> | random?
> |
> | This is what Utts and her coauthors were, it seems, trying to do.
> | However, when the tests were not significant at the chosen level they
seem
> | to have fallen back on inferring (**) directly from (*).
> |
> | -Robert Dawson
> |
> |
> |
> |
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>
>
>
>
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This list is open to everyone. Occasionally, less thoughtful
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