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DATA ANALYSIS FOR SIMULATION EXPERIMENTS: APPLICATION OF A DISTRIBUTION‐FREE MULTIPLE COMPARISONS PROCEDURE
Author(s) -
Cooley Belva J.,
Cooley John W.
Publication year - 1980
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1980.tb01153.x
Subject(s) - computer science , normality , independence (probability theory) , rank (graph theory) , blocking (statistics) , statistical hypothesis testing , mathematical optimization , data mining , statistics , mathematics , computer network , combinatorics
The analysis of simulation data in the evaluation of competing alternatives presents a problem for the analyst using conventional statistical techniques. The assumptions of normality and common variances generally cause difficulties since many classes of simulation experiments typically violate both of these assumptions. In addition, the analyst is usually interested in comparing competing alternatives using environments that are as close to identical as possible. In these situations, blocking is desirable and can usually be accomplished by using common random number streams. This paper discusses a distribution‐free method based on Friedman's rank sums test that can be used to analyze simulation results exhibiting the above characteristics. The procedure requires only independence within treatments.