Bootstrapping with Replacement

Question

I'm reading a paper and am confused with their described Bootstrap Method. the text says:

the uncertainties associated with each stacked flux density are obtained via the bootstrap method, during which random subsamples (with replacement) of sources are chosen and re-stacked. The number of sources in each subsample is equal to the original number of sources in the stack. This process is repeated 10000 times in order to determine the representative spread in the properties of the population being stacked.

So, say I have 50 values. I find the average of these values. According to this method, I would get a subsample from this original 50 population and find that average, and repeat this 10,000 times. Now, how would I get a subsample "equal to the original number of sources in the stack" without my subsample BEING EXACTLY THE SAME AS THE ORIGINAL, AND THUS THE EXACT SAME MEAN, WHICH WOULD TELL US NOTHING!?

Answer 1

you can reuse values. So if i have ABCDE as my values, i can bootstrap with AABCD, etc. I can use values twice, that is the key