Keywords
symmetric normal distribution
sample size
Monte Carlo simulation
Kolmogorov-Smirnov test
How to Cite
Abstract
The use of nonparametric tests is recommended when the data do not meet the assumptions of normality and homoscedasticity. However, the assumptions of normality of the data or the use of goodness of fit tests that are not appropriate for the assessed sample are common aspects. In many cases, this implies the use of statistical tests unadjusted for the real data distribution and, consequently, the establishment of inaccurate conclusions. Therefore, in this paper the detection power of five tests of goodness of fit (Kolmogorov-Smirnov-Lilliefors, Kolmogorov-Smirnov, Shapiro-Wilk, Anderson-Darling and Jarque-Bera) in symmetric distributions is analysed in six sample sizes between 30 and 1000 participants generated by Monte Carlo simulation. Results show a marked conservative tendency as the sample size becomes larger. Regarding sample sizes to detect non-normality: analysing small samples the best results are provided by Kolmogorov-Smirnov-Lilliefors and Anderson-Darling tests, if the sample is medium-sized (200 participants) the Kolmogorov-Smirnov, and when samples are over 500 participants the Shapiro-Wilk test is recommended. In addition, the classic test of Kolmogorov-Smirnov is considered absolutely ineffective regardless the sample size.This journal is registered under a Creative Commons Attribution 4.0 International Public License. Thus, this work may be reproduced, distributed, and publicly shared in digital format, as long as the names of the authors and Pontificia Universidad Javeriana are acknowledged. Others are allowed to quote, adapt, transform, auto-archive, republish, and create based on this material, for any purpose (even commercial ones), provided the authorship is duly acknowledged, a link to the original work is provided, and it is specified if changes have been made. Pontificia Universidad Javeriana does not hold the rights of published works and the authors are solely responsible for the contents of their works; they keep the moral, intellectual, privacy, and publicity rights. Approving the intervention of the work (review, copy-editing, translation, layout) and the following outreach, are granted through an use license and not through an assignment of rights. This means the journal and Pontificia Universidad Javeriana cannot be held responsible for any ethical malpractice by the authors. As a consequence of the protection granted by the use license, the journal is not required to publish recantations or modify information already published, unless the errata stems from the editorial management process. Publishing contents in this journal does not generate royalties for contributors.