The article discusses different criteria used in statistical hypothesis testing, focusing on nonparametric methods like Pearson and Kolmogorov criteria. These criteria help researchers determine if their experimental data matches theoretical distributions. Pearson criterion is used for large sample sizes to test if the data follows a specific distribution, while Kolmogorov criterion compares empirical and theoretical distributions to find the maximum difference. Pearson criterion is more precise as it uses all experimental data. Overall, nonparametric criteria like Pearson and Kolmogorov are widely used in analyzing data and testing statistical hypotheses.