Finance has two major limitations that prevent it from becoming a science, unlike physics, chemistry or biology. These two limitations, Popper’s falsifiability criterion and complexity in the changing financial system, force researchers to rely on backtesting when creating investment algorithms. There are three types of backtests, which includes the walk-forward method, the resampling method, and the Monte Carlo (MC) method. In this paper, Lopez de Prado argues the MC method as the most useful of the three types of backtests. The MC method is further discussed with a practical example, a discussion of its advantages and criticisms, and finally a deeper dive into a key part of MC analysis referred to as the data-generating process (DGP).