# Infer a population, and hence a sampling distribution, from a sample

I'm studying econometrics (i.e. linear regression...) and I have a question about statistical inference concerning the coefficients of the regression.

In order to give ourselves an idea of what the sampling distribution of a coefficient is, there are a bunch of clunky and not very realistic hypotheses to be made. So I was wondering if there is a technique that exists, that would allow us to generate an artificial population, taken the characteristics of the distribution of the sample (let's say it's a very large sample)?

Then all we would have to do is to repeatedly take equally-sized random samples from this artificial population, and there's our sampling distribution, from which we would be able to make all the inference tests we'd like.

Is it possible to do something like this? Thanks

• What you're describing is basically the idea behind bootstrapping. en.wikipedia.org/wiki/Bootstrapping_(statistics) – dsaxton Aug 31 '16 at 15:57
• Ok, I heard the name before but did not know what it meant; do you know of any good tutorials on the subject? Thanks – EBassal Aug 31 '16 at 16:39