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Statistical Analysis

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One of the crucial parts of a simulation study is the design and data analysis of simulation tests. In simulations, they use traditional techniques for statistical analysis, such as sampling data in order to interpret results. Statistical analysis can be used to explore the results; for instance, to study the distribution of values for a specific feature or to identify outliers (maximal or minimal values). One more usage of statistical analysis is summarizing data basing on sample datasets retrieved from simulations. One can also use statistical analysis to recognize and confirm patterns (Statistical Analysis, n.d.).

A simulation is usually a computer-based testing. Therefore, if the output of a simulation research is to have any implication, appropriate statistical methods must be used for design and analysis of the simulations (Law, 1983). The aim of statistical analysis is to identify the regular (feasible) behavior of a simulation and differentiate it from irregular (improbable) behavior. In other words, statistical analysis aims to differentiate what happens regularly from what simply happens accidentally. For instance, if a simulation simulates traffic and a car gets into the ditch, a statistical analysis would investigate whether such a crash occurs often or if this happened by chance and does not happen on a regular basis.

Statistical analysis is important to identify the major factors in simulation. Conducting the analysis of variance enables one to confirm the correct selection of important factors. By identifying critical factors, it is possible to make more and correct decisions. Simulations allow using “real world” scenarios for statistical analysis. The application of statistical analysis to the simulation can provide relevant knowledge for real life scenarios.

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