Summary
Simulation experiments are mostly data-dependent and thus perfectly suited for a data scientist. Different kinds of simulation techniques have been mentioned in this chapter. They are discussed in detail in the next chapters. We mentioned that simulation can be applied almost everywhere to show the properties and performance of methods, to make predictions, and to assess statistical uncertainty. We learned that no general approach exists and that quite different methods exist for different tasks, data sets, and problems. It's up to the data scientist and statistician to choose the right simulation approach.
Whenever computational power is an issue, remember that almost any simulation can be run in a parallel manner, and modern software is ready for this task.
In practice, one should not ask the question "Why did you use simulation?" to somebody who has applied simulation techniques, but rather "Why didn't you use simulation?" to somebody who did not.
