There are scientific applications for this kind of procedure. If an experiment has a noise source with a known frequency distribution, you can simulate the expe

There are scientific applications for this kind of procedure. If an experiment has a noise source with a known frequency distribution, you can simulate the experiment by generating many thousands of realizations of noise superimposed with your (expected) signal. The variance in your measurement introduced by the noise can be used to assess the systematic uncertainty of the experiment. For example, in ground-based experiments that measure the cosmic microwave background radiation, there is a substantial foreground noise from the atmosphere that can be modeled as a 1/f distribution. And actually the observations themselves are subject to a random variance (see cosmic variance) due to the fact that we get to observe the early universe from only one point in space. So you can use a similar trick to sample many random realizations of the CMB for given physical constants, and decide if our one-off observation is compatible with the theory.