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Yadgarov M.Ya.

Federal Scientific and Clinical Center of Intensive Care and Rehabilitation

Kuzovlev A.N.

Federal Scientific and Clinical Center of Intensive Care and Rehabilitation

Berikashvili L.B.

Federal Scientific and Clinical Center of Intensive Care and Rehabilitation

Baeva A.A.

Federal Research and Clinical Center for Intensive Care and Rehabilitation

Likhvantsev V.V.

Federal Scientific and Clinical Center of Intensive Care and Rehabilitation

Importance of data distribution normality test: theory and practical guide

Authors:

Yadgarov M.Ya., Kuzovlev A.N., Berikashvili L.B., Baeva A.A., Likhvantsev V.V.

More about the authors

Journal: Russian Journal of Anesthesiology and Reanimatology. 2021;(2): 136‑142

DOI: 10.17116/anaesthesiology2021021136

Views: 7782

Downloaded: 327

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Table of contents

To cite this article:

Yadgarov MYa, Kuzovlev AN, Berikashvili LB, Baeva AA, Likhvantsev VV. Importance of data distribution normality test: theory and practical guide. Russian Journal of Anesthesiology and Reanimatology. 2021;(2):136‑142. (In Russ.)
https://doi.org/10.17116/anaesthesiology2021021136

Подписка 2025 (АиР)
МСФ на ЯМ
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Testing of data distribution normality is an obligatory element of clinical trials and the «cornerstone» of medical statistics. In the second part of a series of the manuscripts on medical statistics, the authors discuss an importance of data distribution normality test regarding the applicability of parametric and non-parametric statistics. Moreover, practical implementation of this stage is presented. The authors substantiate the advantages and disadvantages of parametric criteria and their nonparametric analogues, give the basic recommendations on the use of more powerful parametric statistics in clinical analysis. The manuscript is intended for physicians and researchers taking their first steps in scientific field.

Keywords:

distribution law
parametric criteria
non-parametric criteria
normality criteria
Box-Cox transformation

Authors:

Yadgarov M.Ya.

Federal Scientific and Clinical Center of Intensive Care and Rehabilitation

Kuzovlev A.N.

Federal Scientific and Clinical Center of Intensive Care and Rehabilitation

Berikashvili L.B.

Federal Scientific and Clinical Center of Intensive Care and Rehabilitation

Baeva A.A.

Federal Research and Clinical Center for Intensive Care and Rehabilitation

Likhvantsev V.V.

Federal Scientific and Clinical Center of Intensive Care and Rehabilitation

Received:

11.09.2020

Accepted:

06.10.2020

List of references:

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  2. Ghosh S, Mitra J. Importance of Normality Testing, Parametric and Non-Parametric Approach, Association, Correlation and Linear Regression (Multiple & Multivariate) of Data in Food & Bio-Process Engineering. In: Mathematical and Statistical Applications in Food Engineering. CRC Press; 2020;112-126. Accessed November 20, 2020. https://books.google.ru/books?hl=ru&lr=&id=6zPNDwAAQBAJ&oi=fnd&pg=PA60&dq=Importance+of+Normality+Testing,+Parametric+and+Non-Parametric+Approach+doi&ots=wHpCeVCgGQ&sig=tan0mZ7Md-RHiskLebSl1Bl-F8M&redir_esc=y#v=onepage&q&f=false
  3. Razali NM, Wah YB. Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. Journal of Statistical Modeling and Analytics. 2011;2(1):21-33. 
  4. Adams WJ. The life and times of the central limit theorem. 2nd edition. American Mathematical Society; 2009. https://doi.org/10.1086/351728
  5. Rana RK, Singhal R, Dua P. Deciphering the dilemma of parametric and nonparametric tests. Journal of Practice of Cardiovascuar Sciences. 2016;2(2):95.  https://doi.org/10.4103/2395-5414.191521
  6. Doerr B, Sutton AM. When resampling to cope with noise, use median, not mean. Proceedings of the Genetic and Evolutionary Computation Conference. 2019;242-248.  https://doi.org/10.1145/3321707.3321837
  7. Dawson R. How significant is a boxplot outlier? Journal of Statistics Education. 2011;19(2). https://doi.org/10.1080/10691898.2011.11889610
  8. Hossain MZ. The use of box-cox transformation technique in economic and statistical analyses. Journal of Emerging Trends in Economics and Management Sciences. 2011;2(1):32-39. Accessed November 20, 2020. https://www.researchgate.net/profile/Mohammad_Hossain30/publication/282603753_The_Use_of_Box-Cox_Transformation_Technique_in_Economic_and_Statistical_Analyses/links/56135b6908aedee13b5c26f4/The-Use-of-Box-Cox-Transformation-Technique-in-Economic-and-Statistical-Analyses.pdf
  9. Shapiro SS, Wilk M.B. An analysis of variance test for normality (complete samples). Biometrika. 1965;52(3/4):591-611.  https://doi.org/10.1093/biomet/52.3-4.591
  10. Yap BW, Sim CH. Comparisons of various types of normality tests. Journal of Statistical Computation and Simulation. 2011;81(12):2141-2155. https://doi.org/10.1080/00949655.2010.520163
  11. Yazici B, Yolacan S. A comparison of various tests of normality. Journal of Statistical Computation and Simulation. 2007;77(2):175-183.  https://doi.org/10.1080/10629360600678310
  12. Kobzar AI. Prikladnaya matematicheskaya statistika. M.: Fizmatlit; 2006. (In Russ.).
  13. Osborne J. Improving your data transformations: Applying the Box-Cox transformation. Practical Assessment, Research and Evaluation. 2010;15(1):12.  https://doi.org/10.7275/qbpc-gk17

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