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Why do we need Bootstrapping?

I'm currently reading Larry Wasserman's "All of Statistics" and puzzled by something he wrote in the chapter about estimating statistical functions of nonparametric models.

He wrote

"Sometimes we can find the estimated standard error of a statistical function by doing some calculations. However in other cases it's not obvious how to estimate the standard error".

I'd like to point out that in the next chapter he talks about bootstrap to address this issue, but since I don't really understand this statement I don't fully get the incentive behind Bootstrapping?

What example is there for when it's not obvious how to estimate the standard error?

All the examples I've seen so far have been "obvious" such as $X_1,...X_n ~Ber(p)$ then $ \hat{se}(\hat{p}_n )=\sqrt{\hat{p}\cdot(1-\hat{p})/n}$