How to choose data from two sets of data to ensure that the choosed data has a better normal feature?

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Your question as posed is incomplete, because you have not specified what the mean and standard deviation are of the distribution that you wish to use as the target. Two histograms may each have an excellent fit to different distributions, such that neither can be faulted as a poor fit to a normal distribution, yet mixing the two would only make things worse. Nor does picking and choosing bins make sense to me even if they are from the same distribution (normally you pool all the data if they are from the same distribution).

This is not a statistics theory forum, but posting a solution to this nonsensical problem would be irresponsible.
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Sent from my phone. Please excuse my brevity.
Hi R mailing listers:

Assume that there are two sets of data  (denoted as A and B) with the
same
size, say 100X1. And I try to select a new set of data (100X1) that has
a
better normal feature  from these two sets. Better normal feature means
that the histogram shape of the constructed set of data is more normal
bell-shaped.  The choose rule is that the i-th element of new set is
from
i-th element of A or B.

Any suggest is greatly appreciated.

Thank you!

David

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<SNIP>
This is not a statistics theory forum, but posting a solution to this nonsensical problem would be irresponsible.
Fortune?

     cheers,

     Rolf
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