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 sample size increases, bootstrapping converges on the correct sampling distribution under most conditions. So I would disagree with Michael R. Chernick's answer that sample size for the bootstrapping does not matter except for very small sample sizes.