meatTheory and practice of meat processingТеория и практика переработки мяса2414-438X2414-441XФГБНУ «Федеральный научный центр пищевых систем им. В.М. Горбатова» РАН10.21323/2414-438X-2022-7-1-42-57meat-209Research ArticleСтатьиMethods for nonparametric statistics in scientific research. Overview. Part 2Methods for nonparametric statistics in scientific research. Overview. Part 2https://orcid.org/0000-0002-8313-4105NikitinaM. A.NikitinaM. A.

Moscow

Marina A. Nikitina, Candidate of technical sciences, docent, leading scientific worker, the Head of the Direction of Information Technologies of the Center of Economic and Analytical Research and Information Technologies

26, Talalikhina str., 109316, Moscow

Tel: +7–495–676–95–11 extension 297

m.nikitina@fncps.ruhttps://orcid.org/0000-0003-4298-0927ChernukhaI. M.ChernukhaI. M.

Moscow

Irina M. Chernukha, Doctor of technical sciences, professor, Academician of the Russian Academy of Sciences, Head of the Department for Coordination of Initiative and International Projects

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imcher@inbox.ruV. M. Gorbatov Federal Research Center for Food Systems of Russian Academy of SciencesРоссияV. M. Gorbatov Federal Research Center for Food Systems of Russian Academy of SciencesRussian Federation202212042022714257Copyright © Nikitina M.A., Chernukha I.M., 20222022Nikitina M.A., Chernukha I.M.Nikitina M.A., Chernukha I.M.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.meatjournal.ru/jour/article/view/209

The use of nonparametric methods in scientific research provides a number of advantages. The most important of these advantages are versatility and a wide range of such methods. There are no strong assumptions associated with nonparametric tests, which means that there is little chance of assumptions being violated, i. e. the result is reliable and valid. Nonparametric tests are widely used because they may be applied to experiments for which it is not possible to obtain quantitative indicators (descriptive studies) and to small samples. The second part of the article describes nonparametric goodness-of-fit tests, i. e. Pearson’s test, Kolmogorov test, as well as tests for homogeneity, i. e. chi-squared test and Kolmogorov-Smirnov test. Chi-squared test is based on a comparison between the empirical (experimental) frequencies of the indicator under study and the theoretical frequencies of the normal distribution. Kolmogorov-Smirnov test is based on the same principle as Pearson’s chi-squared test, but involves comparing the accumulated frequencies of the experimental and theoretical distributions. Pearson’s chi-squared test and Kolmogorov test may also be used to compare two empirical distributions for the significance of differences between them. Kolmogorov test based on the accumulation of empirical frequencies is more sensitive to differences and captures those subtle nuances that are not available in Pearson’s chi-squared test. Typical errors in the application of these tests are analyzed. Examples are given, and step-by-step application of each test is described. With nonparametric methods, researcher receives a working tool for statistical analysis of the results.

The use of nonparametric methods in scientific research provides a number of advantages. The most important of these advantages are versatility and a wide range of such methods. There are no strong assumptions associated with nonparametric tests, which means that there is little chance of assumptions being violated, i. e. the result is reliable and valid. Nonparametric tests are widely used because they may be applied to experiments for which it is not possible to obtain quantitative indicators (descriptive studies) and to small samples. The second part of the article describes nonparametric goodness-of-fit tests, i. e. Pearson’s test, Kolmogorov test, as well as tests for homogeneity, i. e. chi-squared test and Kolmogorov-Smirnov test. Chi-squared test is based on a comparison between the empirical (experimental) frequencies of the indicator under study and the theoretical frequencies of the normal distribution. Kolmogorov-Smirnov test is based on the same principle as Pearson’s chi-squared test, but involves comparing the accumulated frequencies of the experimental and theoretical distributions. Pearson’s chi-squared test and Kolmogorov test may also be used to compare two empirical distributions for the significance of differences between them. Kolmogorov test based on the accumulation of empirical frequencies is more sensitive to differences and captures those subtle nuances that are not available in Pearson’s chi-squared test. Typical errors in the application of these tests are analyzed. Examples are given, and step-by-step application of each test is described. With nonparametric methods, researcher receives a working tool for statistical analysis of the results.

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The authors declare that there are no conflicts of interest present.