<abstract xmlns="http://www.w3.org/1999/xhtml">

<bold>Background</bold>: 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.
<bold>Methods</bold>: 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).
<bold>Results</bold>: 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.
<bold>Conclusion</bold>: 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 &amp; 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.
</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.

Normality assessment, few paradigms and use cases

Department of Medical Informatics and Biostatistics

George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Targu Mures

Revista Romana de Medicina de Laborator

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Normality assessment, few paradigms and use cases"}

Background: The importance of applying the normality tests is underlined by the way of continuing the statistical protocol for numerical data within...

Normality assessment, few paradigms and use cases

Published Online: Jul 18, 2022

Page range: 251 - 260

Received: May 26, 2022

Accepted: Jun 16, 2022

DOI: https://doi.org/10.2478/rrlm-2022-0030

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

© 2022 Călin Avram et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Normality assessment, few paradigms and use cases

Published Online: Jul 18, 2022

Page range: 251 - 260

Received: May 26, 2022

Accepted: Jun 16, 2022

DOI: https://doi.org/10.2478/rrlm-2022-0030

Keywordsnormality test, Gaussian distribution, Q-Q plot diagrams

© 2022 Călin Avram et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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