This study compares the relative power and robustness of the chi-square and Kolmogorov statistics with both the linear score scale and equal areas models. It is limited to the situation in which the mean and standard deviation are fixed by the hypothesis (a necessary constraint with the Kolmogorov tests). Two tables are presented which report the findings for the null hypothesis and the findings for the false hypothesis (sampling from a uniform distribution and testing for normality). In each case the table entry is the number of rejections in 10,000 samples. Conclusions of the study proved the chi-square equal areas model to be superior to the chi-square linear score scale model and to both the Kolmogorov tests. (Author/LS)