I'm not really arguing for using the pooled stdev in this case, I'm just trying to find out the reasons for significance testing procedures.
I think that what were discussing here is if we should use CIs BOTH for stating effect sizes with errors AND for hypoyhesis testing. I read a book by Michael Smithson called Statistics with Confidence (SAGE, 2000). He's using CIs through the whole book in formulations of hypothethis testing. It was really nice reading and I believe students would appreciate the clearness of using fewer formulae for SEs. But then I think we also have to kill darlings like Pearson's Chi Sq. Rolf D > At 04:26 PM 11/15/01 +0100, Rolf Dalin wrote: > > > >The significance test produces a p-value UNDER THE CONDITION > >that the null is true. In my opinion it does not matter whether we > >know it isn't true. It is just an assumption for the calculations. And > >these calculations do not produce exactly the same information as the CI > >for the difference. They state in some sense, if the procedure was > >repeted, how probable it would be to ... etc. > > this might make sense if the sample p*q values were the same for BOTH > samples ... but if they are not (which will almost always be the case in > real data) ... then you already have SOME evidence that the null is > perhaps not true (of course, we know that it is not exactly true anyway > ... so that sort of tosses out the notion of pooling so as to get a better > estimate of a COMMON variance) > > earlier in their presentation, moore and mccabe say that they prefer to > use a CI to test some null in this case ... but, if one did a z test with > the unpooled estimator for standard error, this would lead to a "valid" > significance test ... HOWEVER ... then they go on to say that INSTEAD, > they will adopt the pooled standard error approach since it is the " ... > more common practice" > > that logic escapes me > > if we can build a CI using the un pooled standard error formula and, find > that to be ok to see if some null value like 0 difference in population > proportions is inside or outside of the CI, i don't see any need to switch > the denominator formula in the z test JUST because we want to use the z > test STATISTIC to test the null > > a little more consistency in logic would seem to be in the best interests > of students trying to learn this ... > > i would still argue that the extent to which you would not be willing to > use the pooled standard error formula in the case of differences in means, > would be the same extent to which you would not be willing to use the > pooled standard error formula when it comes to differences in proportions > ... i don't see that the logic really is any different > > but, this is just my opinion > > > _________________________________________________________ > dennis roberts, educational psychology, penn state university > 208 cedar, AC 8148632401, mailto:[EMAIL PROTECTED] > http://roberts.ed.psu.edu/users/droberts/drober~1.htm > ************************************************** Rolf Dalin Department of Information Tchnology and Media Mid Sweden University S-870 51 SUNDSVALL Sweden Phone: 060 148690, international: +46 60 148690 Fax: 060 148970, international: +46 60 148970 Mobile: 0705 947896, intnational: +46 70 5947896 mailto:[EMAIL PROTECTED] http://www.itk.mh.se/~roldal/ ************************************************** ================================================================= Instructions for joining and leaving this list and remarks about the problem of INAPPROPRIATE MESSAGES are available at http://jse.stat.ncsu.edu/ =================================================================
