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In article <1992Aug18.152355.16237@news.cs.brandeis.edu> mokaba@binah.cc.brandeis.edu writes:
>could somebody explain in English what the true effect of sample size
>is on maximulikelihood estimates.  I have been playing around with 
>LISREL and have a little problem conceptualizing the effect of sample
>size on the estimates I get using maximumlikelihood estimators.  Are the
>effects comming in becuase of the effect of sample size on the standard
>error of estimates or is there something I am too dense to see. 
>I have also learn that the problem with large sample in LISREL is that they
>lead to even small effect being significant... or something like that..
>Furthermore, is a sample of 103 good enough for a full model estimation.
>i.e running the Mesurement equations at the same time as the Structural 
>model.  
In English, a larger sample is very likely to have a more concentrated
likelihood function.  Among other things, this reduces the "standard error"
of estimates.

As for statistical significance, neither I nor anyone else has been able
to come up with any good reason to use it; it is a leftover from the time
when it was misinterpreted.  

As for a sample of 103 being good enough for ..., in some cases samples
of 10 are quite adequate for purposes, and in some cases samples larger
than the entire world population are inadequate.

This is not an attempt at obfuscation, but the proper use of statistics
is not that simple; see a good mathematical statistician.
-- 
Herman Rubin, Dept. of Statistics, Purdue Univ., West Lafayette IN47907-1399
Phone: (317)494-6054
hrubin@pop.stat.purdue.edu (Internet, bitnet)  
{purdue,pur-ee}!pop.stat!hrubin(UUCP)