Abstract
Computer simulation is the process of designing and creating a computerized model of a real or proposed system for the purpose of conducting experiments to give us a better understanding of the behavior of the system under study for a given set of condition. Simulation studies have been used to investigate the characteristics of systems, to assess and analyze risks, for example, the probability of a machine breakdown. Hence, simulation is a valuable tool for risk management. However, estimates of measure of system performance from stochastic simulation are themselves random variables and are subject to sampling error. One must take into account sampling error when making inferences concerning system performance. We discuss how statistical techniques are applied in simulation output analysis, e.g., initialization bias reduction, tests of independence, confidence interval estimation, and quantile estimation. A carefully selected quantiles can reveals characteristic of the underlying distribution. These statistical techniques are key components of many simulation studies.