Monte Carlo simulation randomly and repeatedly generates values for uncertain variables to simulate a model. The values for each assumption’s probability distribution are random and totally independent. In other words, the random value selected for one trial have no effect on the next random value generated.
Monte Carlo simulation was named for Monte Carlo, Monaco, whose casinos feature games of chance such as roulette, dice, and slot machines, all of which exhibit random behavior.
Such random behavior is similar to how Monte Carlo simulation selects variable values at random to simulate a model. When you roll a die, you know that a 1, 2, 3, 4, 5, or 6 will come up, but you do not know which for any particular trial. It is the same with the variables that have a known range of values and an uncertain value for any particular time or event (for example, interest rates, staffing needs, stock prices, inventory, phone calls per minute).
Using Monte Carlo sampling to approximate the true shape of the distribution requires more trials than Latin Hypercube.
Use Monte Carlo sampling to simulate “real world” what-if scenarios for your spreadsheet model.