The tests proposed for the one-way anova under unequal variances and a simulation study

Classical F-test to compare several populations means depends on the assumptions of homogenety of populations variances and normality. When these assumptions-especially the equality of variance-is dropped, classical F-test fails to reject the null hypothesis even the data actually provides strong evidence to do so. In this can be considered as a serious problem in some applications especially when the sample size is not large. For this problem a large number of tests are available in the literature. In this study tests in Brown-Forsythe, Generalized F, Parametrik Bootstrap, Scott-Smith, One Stage, One Stage Range, Welch, Xu-Wang are introduced and usege of these tests are given with a real data. Also a simulation study is perform to compare these tests in different combination of variance, means, population number and sample size.