I have read/heard many times that the sample size of at least 30 units is considered as "large sample" (normality assumptions of means usually approximately holds due to the CLT, ...).  Therefore, in my experiments, I usually generate samples of 30 units. Can you please give me some reference which should be cited when using sample size 30?
The choice of n = 30 for a boundary between small and large samples is a rule of thumb, only. There is a large number of books that quote (around) this value, for example, Hogg and Tanis' Probability and Statistical Inference (7e) says "greater than 25 or 30".

That said, the story told to me was that the only reason 30 was regarded as a good boundary was because it made for pretty Student's t tables in the back of textbooks to fit nicely on one page.  That, and the critical values (between Student's t and Normal) are only off by approximately up to 0.25, anyway, from df = 30 to df = infinity.  For hand computation the difference didn't really matter.

Nowadays it is easy to compute critical values for all sorts of things to 15 decimal places.  On top of that we have resampling and permutation methods for which we aren't even restricted to parametric population distributions.

In practice I never rely on n = 30.  Plot the data.  Superimpose a normal distribution, if you like.  Visually assess whether a normal approximation is appropriate (and ask whether an approximation is even really needed).  If generating samples for research and an approximation is obligatory, generate enough of a sample size to make the approximation as close as desired (or as close as computationally feasible).
