Voting to close, but a simple test will answer your question
set.seed(1)
# we know these data are normally distributed...
dat <- rnorm(500,10,1)
# let's compute some fits...
require(MASS)
fits <- list(
no = fitdistr(dat,"normal"),
lo = fitdistr(dat,"logistic"),
ca = fitdistr(dat,"cauchy"),
we = fitdistr(dat, "weibull")
)
# get the logliks for each model...
sapply(fits, function(i) i$loglik)
no lo ca we
-718.3558 -722.1342 -806.2398 -741.2754
So the loglik that is the largest value is the one that indicates the best fit. We put in normally distributed data, and the loglik for the normal fit is the largest.
You might also find this image useful, from http://people.stern.nyu.edu/adamodar/pdfiles/papers/probabilistic.pdf
