When working with sampled data, we need to produce the same descriptive statistics that we do when working with populations of data. Of course, these will just be estimates, and there is error inherent in those estimations.
Bootstrapping is a way to estimate the distribution of a population. Bootstrapping works by taking a sample from the population and repeatedly resampling with replacement from the original sample. With replacement means that the same observation is allowed in the sample more than once. After each re-sampling, the statistic is computed from the new sample. From this we estimate the shape of the distribution of a value in the population.
We can use bootstrapping when the sample we're working with is small, or even when we don't know the distribution of the sample's population.