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If you want to create a bootstrapped dataset, you need to sample with replacement N times. Since a bootstrapped dataset is obtained by sampling N times from a dataset of size N, we need to sample N times to find the probability that a particular row is not chosen in a given bootstrapped dataset.
Simple random sample with replacement (SRSWR): In this scenario, n units of the sample are drawn one at a time from the population. The units collected at each draw are replaced in the population so that the probability of obtaining any unit in any draw is 1/N.
Sampling with replacement has two advantages over sampling without replacement as I see it: 1) You don't need to worry about the finite population correction. 2) There is a chance that elements from the population are drawn multiple times - then you can recycle the measurements and save time.
In sampling with replacement the mean of all sample means equals the mean of the population: When sampling with replacement the standard deviation of all sample means equals the standard deviation of the population divided by the square root of the sample size when sampling with replacement.
If I sample two with replacement, then I first pick one (say 14). I had a 1/7 probability of choosing that one. Then I replace it. Then I pick another.