A Monte Carlo simulation uses repeated sampling to obtain the statistical properties of some phenomenon (or behavior). For example: drawing a large number of pseudo-random uniform variables from the interval [0,1] at one time, or once at many different times, and assigning values less than or equal to 0.50 as heads and greater than 0.50 as tails, is a Monte Carlo simulation of the behavior of repeatedly tossing a coin.

The main idea of this method is that a phenomenon is simulated multiple times on a computer by generating random numbers and the results are aggregated to provide statistical summaries associated with the phenomenon. Sawilowsky lists the characteristics of a high-quality Monte Carlo simulation.

#Conclusions

Throughout this module we have learned how to carry out a simulation using Monte Carlo methods, and to make inferences from the results of the simulated experiments. The concepts acquired have been applied to concrete experiments, developing the steps of a Monte Carlo simulation in very different contexts. In the Lab of this module you will face problems with similar dynamics, but keep in mind that each Monte Carlo simulation is totally unique, and you will have to develop the code from scratch.