RRML - Normality assessment, few paradigms and use cases
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Concept, Design & Programming
Dr. Adrian Man
   
 
Nr. 30(3)/2022 DOI:10.2478/rrlm-2022-0030
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Normality assessment, few paradigms and use cases

Călin Avram, Marius Mărușteri

Correspondence should be addressed to: Călin Avram

Abstract:

Background: The importance of applying the normality tests is underlined by the way of continuing the statistical protocol for numerical data within inferential statistics, respectively by the parametric or non-parametric tests that we will apply further on. Methods: To check the calculation mode, we used sets of random values and we performed the normality assessment using statistical calculation programs. We took non-Gaussian data (n = 30, n = 50, n = 100, n = 500) and Gaussian data (n = 30, n = 50, n = 100, n = 500) for which we checked the normality of the data. Data chosen for this study were most representative for each batch (n). Results: The application of normality tests to the data under study confirms that the data are non-Gaussian for the first data set. For the Gaussian data sample, the verification of normality is confirmed by the results. Conclusion: For data up to 50 subjects, it is recommended to apply the Shapiro-Wilk test, but also to apply graphical methods to confirm the accuracy of the result. If the data samples have more than 50 values, the D’Agostino & Pearson omnibus normality test should be applied and if the statistical program does not contain this test, the Shapiro-Wilk test can be applied (in the case of SPSS). Graphical methods, although they require some experience, are useful for identifying the normality of distributions with a small number of data.

Keywords: normality test, Gaussian distribution, Q-Q plot diagrams

Received: 26.5.2022
Accepted: 16.6.2022
Published: 30.6.2022

 
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How to cite
Avram C, Mărușteri M. Normality assessment, few paradigms and use cases. Rev Romana Med Lab. 2022;30(3):251-9. DOI:10.2478/rrlm-2022-0030