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.  

How well the CI  will fit a set of means is going to depend on 
how well "asymptotic normal"  is reached.   In your instance,
that might depend on how correlated or dependent
the values are, in addition to depending on the Ns.

 - In ANOVA contexts, one occasionally computes an 
average N as the "harmonic mean" of the Ns 
(reciprocal of the average reciprocal).  Your Ns are 
similar enough that there would be little variation from
the simple average;  but I don't see where you have the
occasion to use those counts at all.

-- 
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
.
.
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