Gaussian distribution and likelihood question

hi,
My question is: is there any method to measure how a data set fits a
Gaussian model? Suppose I have one dataset A which has N elements and
another dataset B which has M elements. From each of the dataset I can
estimate one Gaussian model, thus I have (mu1, sigma1) for A and (mu2,
sigma2) for B. How can I tell which dataset fits the Gaussian model
better? (skewness and kurtosis does not tell about fitness)
What I am thinking is from the Gaussian model I can generate the same
amount of elements and those elements should fit the model best. Then
I can compare the log-likelihood of the real dateset to the generated
dataset. But, to produce a psudo dataset from a Gaussian model is not
efficient. My next question is: is there a 1-step formula to get the
log-likelihood of the "theorically perfect Gaussian dataset" with N
elements? Basically, find the most possible log-likelihood of N
elements from Gaussian model(mu, sigma).
Thanks for your comments.
zl2k

.

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