The Meaningfulness of Statistical Significance Tests in the Analysis of Simulation Results

K. G. Troitzsch
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引用次数: 1

Abstract

Thisarticlediscussesthequestionofwhethersignificancetestsonsimulationresultsaremeaningful atall.Itisalsoarguedthatitistheeffectsizemuchmorethantheexistenceoftheeffectiswhat matters.Itisthedescriptionofthedistributionfunctionofthestochasticprocessincorporatedinthe simulationmodelwhichisimportant.Thisisparticularlywhenthisdistributionisfarfromnormal, whichisparticularlyoftenthecasewhenthesimulationmodelisnonlinear.Tothisend,thisarticle usesthreedifferentagent-basedmodelstodemonstratethattheeffectsofinputparametersonoutput metricscanoftenbemade“statisticallysignificant”onanydesiredlevelbyincreasingthenumber ofruns,evenfornegligibleeffectsizes.Theexamplesarealsousedtogivehintsastohowmany runsarenecessarytoestimateeffectsizesandhowtheinputparametersdetermineoutputmetrics. KeywoRdS Cumulative Periodogram, Deduction, Microspecification, Multilevel Model, Random Number Generator, Statistical Significance Level, Stochastic Process, Validity
统计显著性检验在仿真结果分析中的意义
Thisarticlediscussesthequestionofwhethersignificancetestsonsimulationresultsaremeaningful atall。Itisalsoarguedthatitistheeffectsizemuchmorethantheexistenceoftheeffectiswhat很重要。Itisthedescriptionofthedistributionfunctionofthestochasticprocessincorporatedinthe simulationmodelwhichisimportant。Thisisparticularlywhenthisdistributionisfarfromnormal, whichisparticularlyoftenthecasewhenthesimulationmodelisnonlinear。Tothisend,thisarticle usesthreedifferentagent-basedmodelstodemonstratethattheeffectsofinputparametersonoutput metricscanoftenbemade " statisticallysignificant " onanydesiredlevelbyincreasingthenumber ofruns,evenfornegligibleeffectsizes。Theexamplesarealsousedtogivehintsastohowmany runsarenecessarytoestimateeffectsizesandhowtheinputparametersdetermineoutputmetrics。关键词累积周期图,演绎,微规格,多层模型,随机数生成器,统计显著性水平,随机过程,效度
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