Dealing with project uncertainties: is the sum of the most-likely estimates equal to the sum of the expected times? I found this document on the internet:  Dealing with Project 
Uncertainties
In this article I read:

In order for uncertainties to be included in the project estimates it
  is  necessary to take into account an optimistic estimate (o), a
  pessimistic  estimate (p), and a most-likely estimate (m). This
  approach is referred to  as stochastic, or statistical estimating, and
  is the correct technique to use  when developing what is known today
  as a PERT (Program Review and  Evaluation Technique)

So to obtain the estimate for 1 task one can use this formula:
$$
 Te = \frac{o+4m+p}{6}
$$
It then presents us with this formula (I had already heard of this formula (in the book The black art of Software Estimation)):
$$
D = \sum T_{e} + Z\sqrt{\sum}\sigma ^{2}
$$
Where $\sum T_{e}$ = the sum of all path expected times and $\sum \sigma ^{2}$ = the sum of all critical path variances and $Z$ is the number of standard deviations of a normal distribution (the standard normal deviate). And D is the project’s duration at a desired level of confidence "Z", such as 1.281 for 90% confidence
To obtain the variance it presents the formula:
$$
Variance = \sigma^{2} = (\frac{p-o}{6})^{2}
$$
The article also says:

Regardless of the shape  formed by the data values of the estimates,
  the shape of the values making  up the sampling distribution of the
  expected times will approach a normal  distribution based on the
  central limit theorem, if the sampling size is  sufficiently large
  (usually 30 or greater). When the sampling size is less  than 30, and
  the Central Limit Theorem cannot be invoked, a tdistribution must be
  used

Now the questions:
¿Is this $D = \sum T_{e} + Z\sqrt{\sum}\sigma ^{2}$ an accurate way to sum estimates? I asked before about file storage estimation, and this formula was not used to provide me with answer... ¿why is that? ¿is it because did not have different estimations with different optimistic, pessimistic and most likely sizes? 
¿If sampling size is less  than 30... how can I use this "tdistribution" to sum estimates ? ¿what is the formula then?
 A: I would say the answer to your first question is "no, this is not an appropriate way to sum the estimates of time for individual tasks".  It would be appropriate if the random element of the time taken to complete each task were independent of the randomness in the other tasks; but this seems unlikely.  Obvious sources of dependence would include:


*

*the initial estimates were all biased in the same direction due to the project manager's pessimism or optimism

*a key resource that is used in more than one task on the critical path is under or over performing, leading to several tasks taking more or less time than predicted


How much of a problem this is is entirely a pragmatic matter and depends on the usual size of the covariance of task times (ie how much dependence there is in the way they differ from their estimates).  
On your second question re t v Z statistics when there are less than 30 estimates of task time to add (I wouldn't call this a "sample" as your reference seems to - it's a bit counter-intuitive, although it could be justified if you twist your brain around enough).  I would say that in fact the t distribution should be used all the time because the variances are all estimated.  However, as n becomes bigger the t distribution becomes effectively identical to a normal distribution.
Wikipedia's article on the t distribution has a table of the critical values, or any stats program (even Excel) can provide it.  As you're only worried about exceeding a certain threshhold, you can use the one-sided values (and this seems to be how you do your Z estimates).  The appropriate degrees of freedom is a bit of a guess (because of the way variances are estimated in the first place) but is probably the number of tasks for which you are adding estimates, minus one.  You'll see that as the degrees of freedom go up, the values of the t distribution approach the Z values you refer to in your comments.
