The article explores how nonparametric goodness-of-fit tests are affected when estimating distribution parameters from the same sample in composite hypotheses testing. The researchers developed more precise models for these tests using maximum likelihood estimates for certain distribution laws. By simulating statistic distributions, they found empirical models that approximate the actual distributions. This work helps improve the accuracy of statistical tests in complex hypothesis testing scenarios.