Welcome to CV!
In bootstrapping, you repeatedly take samples with replacement from the original sample. The general idea behind this is that if you can estimate the uncertainty in your sample by asking the question: What if I didn't observe this observation, or that one, or if I observed this observation more than once?
You do this, say, $B = 1,000$ times, and end up with $1,000$ slightly different estimates of your statistic of interest. Depending on how strongly the calculated statistic is affected by this, the variance of your bootstrapped statistic will be larger.
In fact, it turns out that the standard deviation of the bootstrapped statistic can be a really good estimator of the standard error of your statistic.
And so, by simply randomly resampling our original sample with replacement, over and over, we have obtained an idea of how precise the estimate is, given that we only have a sample of the population.
Of course, if you can measure the entire population, then there is no point in bootstrapping.