An important problem in water resources engineering is the estimation of the flood magnitude for a certain return period. In flood frequency analysis, an assumed probability distribution is fitted to the available sample data to estimate the flood magnitude at the upper tail corresponding to return periods which are usually much larger than the record length. In most cases, the selection of an appropriate probability distribution is based on goodness-of-fit tests. However, previous goodness-of-fit tests give equal weight to differences between empirical and theoretical distribution functions corresponding to all the observations. In this study, the modified Anderson-Darling test statistics which can give different weights to given data are provided using simulation. And the regression equations for the modified Anderson-Darling test statistics are derived as a function of sample sizes and significance levels for the generalized extreme value and generalized logistic distributions.