What is the Central Limit Theorem and how does UnRiskIT manage it?
The central limit theorem (CLT) states that the sum of a large number of independent and identically-distributed random variables will be approximately normally distributed (i.e., following a Gaussian distribution, or bell-shaped curve) if the random variables have a finite variance. Simply put, if you have a large number of variables in a Monte Carlo simulation then you will experience a narrowing of the overall outcome as the number of variables increase. And the range of outcomes might not be acceptable/reasonable to you. To address this issue we can make some intelligent correlations between variables, making them dependent in some manner. You may logically ascertain that as time increases with a particular activity then the trouble associated with that activity may increase as well. UnRiskIT provides for a logical and transparent means to define these correlations which results in a more realistic range of possible outcomes.
