The resampling process creates many possible samples that a study could have drawn. The various combinations of values in the simulated samples collectively provide an estimate of the variability between random samples drawn from the same population. The range of these potential samples allows the procedure to construct confidence intervals and perform hypothesis testing. 

Importantly, as the bootstrap sample size increases, bootstrapping converges on the correct sampling distribution under most conditions. 

In regards to your question on:
"This is merely an idea on how to determine how large your original sample size needs to be in order to be reasonably certain that the sample distribution corresponds with the population distribution."

This is dependent on the specific problem that you are examining and is not dependent on the bootstrap sample size. The purpose of the bootstrap sample is merely to obtain a large enough bootstrap sample size, usually at least 1000 in order to obtain with low MC errors such that one can obtain distribution statistics on the original sample e.g. 95% CI. But this cannot guarantee that the original sample taken is representative of the actual population distribution.