It’s relatively easy to make a decision if you know the consequences of each alternative. Unfortunately, you don't have a crystal ball. You don't know how things will work out, how long tasks will take, how much things will cost, how well your investments will do, how long you will live, or what will go wrong. Things that worked in the past may not work in the future. As they say in the disclaimers for investments, “past performance is no guarantee of future performance.”
The good news is that even if you don't know exactly what will happen in the future, you can think about what might happen, and even assign probabilities to it. You can then use random numbers in your models to look at possible outcomes. The random models will randomly assign, say, the failure rate of a risky investment or the annual growth rate of a mutual fund. Then the results you see will be representative of what could happen. For example, a standard model might assume an investment return of 6% per year. That model will always give you the same answer. A random model might instead assume an average return of 6%, with a standard deviation of 3%. Then some years you could get 5% or 8% or even lose money. The luck of the draw will determine how well your investments do over the long term.
Every time you run your model with new random numbers you'll see a different possible outcome. This represents something that could happen. How would you handle it? Double click any empty cell and press Enter and you'll see another possibility. If you continue doing this you'll see representative outcomes, with more likely outcomes appearing more often. Think about these possible outcomes. Do the good results justify the risk of the bad results?
Monte Carlo models
The random models let you look at several random outcomes one at a time. Often you would like to see a lot of outcomes, to get a feel for the possibilities or estimate probabilities. It turns out that you can use Excel’s tables to run hundreds of cases, one after the other, and list and summarize the results. This technique of running multiple random cases is called Monte Carlo modeling, after the famous gambling center. It’s used increasingly in investment analysis to understand risks. The spreadsheets use 100 replications to give a rough estimate of probabilities of outcomes. If you want more replications for more accuracy replace the table with a larger one.
Even in a simple little Monte Carlo model you may be annoyed with the time it takes to recalculate everything any time you change something. It even recalculates when it’s not active and you change some other spreadsheet. This is especially frustrating when you are modifying the model. Fortunately, you can turn off recalculation under Preferences. This is either in the File menu (Windows) or the Excel menu (Mac). Because Monte Carlo models take so long to run, set up and analyze the model first in a random (single iteration) version.