Rich Ulrich <[EMAIL PROTECTED]> wrote in message 
news:<[EMAIL PROTECTED]>...
> On Wed, 2 Oct 2002 15:21:14 +0100, "Jacek Gomoluch"
> <[EMAIL PROTECTED]> wrote:
> 
> > I have a question concerning the calculation of the confidence interval of
> > the mean of a measured variable.
> > 
> > I am running several (m>30) simulation runs with same parameters but
> > different random seeds. In each of these simulation runs, n samples (usually
> > several thousand) of a measured variable are taken. In my simulations, the
> > samples within each simulation run can not be assumed to be independent.
> > However, according to the literature I read so far, the sample means of the
> > different simulation runs are normally distributed (due to the central limit
> > theorem). In the literature, it is assumed that the number of samples (n) is
> > the same for each simulation run.
> > 
> > With this assumption the confidence interval of the mean is calculated by:
> > 
> > overall_sample_mean +/- z_value* ( standard_dev_of_the_sample means)
> > 
> > However, in my experiments the number of samples (n) is not the same for
> > each simulation run, but slightly different (e.g. 3105, 2934, 3050,...).
> > 
> > My question: does this matter? Or can I still use the above formula?
> 
> I hope you are computing the  <SD_of_means>    by looking at 
> those means as a set of numbers --  and *not*  by looking at 
> the formula for Standard Error.
> 
> So, you can take the SD  of any set of numbers, and apply
> the formula for their CI.  

In this case each number is a mean which has it's own CI. It seems to
me that the grand CI should consist of two parts: That due to the
spread of the individual measurements about the group mean and that
due to the spread of the group means about the grand mean.

If not, what am I missing?

Hope this helps.

Greg
.
.
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