Use MLEs to choose the 'most likely' from a set of distributions? Would it be valid to choose a probability distribution for assumptions based on the actual maximum likelihood of various distributions derived from MLE estimates? 
For example, suppose I find MLE estimates of a normal distribution $[ \mu , \sigma^2 ] = [1,4]$, which I then substitute (along with $n$) back into the likelihood formula $L_N =(2 \pi \sigma^2 )^{-n/2} \exp(- \sum_{i=1}^n [(x_i - \mu)^2/(2 \sigma^2 )] )$ to obtain a number $\hat{L}_N$. Could I then go through the same process to obtain $\hat{L}_B$ for a beta distribution and directly choose $\max \{ \hat{L}_N , \hat{L}_B \}$ in deciding which of the two distributions would be more appropriate in further analysis?
 A: If you were to do that then you would be effectively just finding the MLE in a single problem where the distributional family you are using is the union of the family of normal distributions and the family of beta distributions.  That is somewhat strange, since the class of distributions you form include two different parametric forms.  Nevertheless, it is perfectly valid, insofar as what you obtain is still an MLE.  Letting $\mathscr{N}$ denote the class of all normal distributions and $\mathscr{B}$ denote the class of all beta distributions, your MLE would be:
$$\hat{F} = \underset{F \in \mathscr{F}}{\text{arg max }} L_\mathbf{x}(F)
\quad \quad \quad \mathscr{F} = \mathscr{N} \cup \mathscr{B}.$$
Taking $\hat{L}_N \equiv \underset{F \in \mathscr{N}}{\text{max }} L_\mathbf{x}(F)$ and $\hat{L}_B \equiv \underset{F \in \mathscr{B}}{\text{max }} L_\mathbf{x}(F)$ gives you:
$$L_\mathbf{x}(\hat{F}) = \underset{F \in \mathscr{F}}{\text{max }} L_\mathbf{x}(F) = \max (\hat{L}_N, \hat{L}_B).$$
Now, it is important to bear in mind that, although this is a valid MLE, you will need to establish whether or not the standard properties of the MLE hold in this case.  Some standard properties of the MLE given in theorems pertaining to parametric models will hold and some will not.  This depends on the required assumptions for these properties, which is complicated in this case.  In determining whether a particular property applies, you will need to examine the proofs of these properties and the required assumptions, and you should bear in mind that in this case you no longer have a class of distributions where there is a smooth "path" between all distributions in the class (in a topological sense).$^\dagger$ 
You can certainly use this MLE as an estimator of the true distribution across a class composed of two distributional families.  Whether this estimator has the desirable statistical properties that hold in simpler cases is a tricky question, and would require a detailed investigation of derivation of those properties in this case.

$^\dagger$ Actually, this part is a little tricky.  It is made more complicated by the fact that the beta distribution is asymptotically normal for a broad class of limiting cases, so there is arguably a smooth path between all distributions in the class, through the asymptotic limits.  Whether this is sufficient to obtain the required properties for proofs of standard properties of the MLE is something that would require investigation.
