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Comparing Robust Test Statistics in Unbalanced Designs In this study, the violations of the assumptions, heteroscedasticity and non-normality are consedered in the one-way ANOVA. Robust Brown-Forsythe (RBF) and robust modified Brown-Forsythe (RMBF) test statistics are developed for the non-normal data assumed to be Weibull distribution with outliers. In the simulation study, using different experimental designs, type I error risks of the improved robust test statistic for the Weibull distribution are obtained with respect to 2 different robust methods which are quantile and least square quantile. Unbalanced sample sizes with homogeneous and heterogeneous variances are considered for k=3,9 groups. The simulations results show up that the proposed robust tests have a good performance.