For doing inference of a population parameter from a sample, under which examples is better to calculate the standard error using bootstrap distribution of the mean than directly using the standard error of the sample?
Let's take the mean of income as an example:
I can take a sample and calculate the income mean, and from the sample I can calculate the standard error using sample_standard_deviation/square_root(n).
I can treat my sample as the "population", and apply the bootstrap, and thus create the distribution of the mean and infer the standard error.
Which are some example on using one over the other?