simulation of t distribution - repeated sampling I am new to simulation exercises in R. I want to create 1000 samples of size 25 from a t distribution with degrees of freedom 10.
Do I need to create a single vector of data from the rt generator, and then sample repeatedly from that? So, for example, I could create the vector:
singlevector <- rt(5000, 10) , which generates data from a t -distribution of size 5000 and df = 10. So, I would treat this as my population and then sample from it. I chose the population size of 5000 arbitrarily here. 
OR, should I create my 1000 samples calling on this random t generator every time?
In other words, create a matrix with 25 rows and 1000 columns, each column containing vector corresponding to a new call of rt(25, 10). 
Thank you.
 A: Your population is the (notionally) infinite population of values you might have got from a t-distribution with 10 df. (In practice a random number generator can only generate a finite number of distinct values, because precision is finite. However this doesn't change the way you should normally look at it.)
Any finite population can't literally be t-distributed (though it may have a distribution-function that's quite close to that of the $t$).
You should not generate fewer values than you will need and then sample those with replacement, as you seem to propose.
While there would be no harm in sampling some larger-than-needed set of t$_{10}$-distributed values (i.e. more than 25000) and then sampling without replacement from those, there's no point in doing so -- simply simulate all the values you need directly from the generator when you need them.
[Personally, if I were doing it, I'd probably simulate 25000 values and arrange them into an object in one go, such as by a call to rt inside a call to matrix, but there's a variety of ways to do it that would reasonable (it's such a small number of values it hardly matters how you do it as long as you don't grow any data structures as you go). If I only needed something computed from each sample I'd probably use replicate instead.]
