An additional complication is that with a large sample size (I assume it is quite large), even quite trivial effects will be found to be "significant" if we test hypotheses. We all know that our usual audience thinks that "significant" means "big and important." And no, they will pay no attention to estimates of effect sizes and are not likely to be satisfied with confidence intervals -- they will ask "but is it SIGNIFICANT?" Sigh.
the point karl is making here canNOT be taken likely ... and, could be used in a nefarious way to "prove" one's position
if one has lots and lots of male and female employees ... then, as karl suggests ... one is very likely to finding ps less than .05 ... in fact, it is almost guaranteed given what we know about the NON equivalence of male and female salaries
however, if you are trying to show that your company is on the up and up ... doing the right thing ... you could examine these data on a small group of males and females ... and perhaps NOT reject the null
in either case ... how has this helped learning about the existence of salary differences amongst males and females? i say that it has helped not
Karl W.
-----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] Sent: Friday, April 04, 2003 8:29 AM To: [EMAIL PROTECTED] Subject: Re: T-test valid when whole population available?
In article <[EMAIL PROTECTED]>, Alan McLean <[EMAIL PROTECTED]> wrote:
>Radford and I effectively made two different assumptions - I that the >population of interest was the population measured, he that it it was >wider than the population measured. With my assumption the t test is >not relevant; with his, its relevance depends on whether the >(sub)population measured can reasonably be considered a random sample >from the population of interest.
Whether the t test in particular is the right tool is a detailed technical issue that would depend on such things as whether it is reasonable to regard the employees as independent (versus, for instance, a whole group of same-sex friends having been hired by the company, because one of them got hired and told the others how nice it was.)
Regarding the more basic question of whether testing for statistical significance is sensible at all, this does of course depend on what one assumes is the population of interest. However, the recurring posts on this topic seem to almost always be for situations in which testing for significance IS appropriate, but somebody starts thinking too hard, saying, "but we've got data on everyone..."
My guess is that situations where testing for significance is NOT appropriate are usually so obvious that nobody gets confused. For instance, suppose that the company is faced with a possible court ruling (sensible or not) that would require it to raise the salaries of female employees to the point where their average is the same as that of the men. The company wants to know how much their payroll would increase if this happened. They collect data on all the salaries, and from that figure out what the payroll increase would be. Nobody would be silly enough to say - "Wait! The difference between male and female salaries isn't statistically significant, so maybe this court ruling won't cost us anything at all..."
Radford Neal . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
_________________________________________________________
dennis roberts, educational psychology, penn state university
208 cedar, AC 8148632401, mailto:[EMAIL PROTECTED]
http://roberts.ed.psu.edu/users/droberts/drober~1.htm
. . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
