Methods for nonparametric statistics in scientific research. Overview. Part 1.

Daily, researcher faces the need to compare two or more observation groups obtained under different conditions in order to confirm or argue against a scientific hypothesis. At this stage, it is necessary to choose the right method for statistical analysis. If the statistical prerequisites are not met, it is advisable to choose nonparametric analysis. Statistical analysis consists of two stages: estimating model parameters and testing statistical hypotheses. After that, the interpretation of the mathematical processing results in the context of the research object is mandatory. The article provides an overview of two groups of nonparametric tests: 1) to identify differences in indicator distribution; 2) to assess shift reliability in the values of the studied indicator. The first group includes: 1) Rosenbaum Q-test, which is used to assess the differences by the level of any quantified indicator between two unrelated samplings; 2) Mann-Whitney U-test, which is required to test the statistical homogeneity hypothesis of two unrelated samplings, i. e. to assess the differences by the level of any quantified indicator between two samplings. The second group includes sign G-test and Wilcoxon T-test intended to determine the shift reliability of the related samplings, for example, when measuring the indicator in the same group of subjects before and after some exposure. Examples are given; step-by-step application of each test is described. The first part of the article describes simple nonparametric methods. The second part describes nonparametric tests for testing hypotheses of distribution type (Pearson’s chi-squared test, Kolmogorov test) and nonparametric tests for testing hypotheses of sampling homogeneity (Pearson’s chi-squared test for testing sampling homogeneity, Kolmogorov-Smirnov test).