Why do statisticians need to know sampling technique Many statistical courses teach sampling techniques, such as rejection sampling and importance sampling, to sample data from a distribution. What are some concrete scenarios where a statistician needs to apply a sampling technique?

One reasonable perspective on statistics / econometrics is that the estimated model should be able to reproduce the data actually observed (Rubin 1984; McFadden, 1989; Gouriéroux et al., 1993; Gelman et al., 2014). In order to do so, sampling from the potential (statistical) model via pseudo-random generators is paramount.
As commented by John Madden, Bayesian statistics is strongly connected with sampling / simulation, since this is often the only feasible way to represent the posterior distribution.
