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Journals / International Journal of Assessment Tools in Education / 2020 / Cilt: 7 - Sayı: 2

Parametric or Non-parametric: Skewness to Test Normality for Mean Comparison

Fatih ORÇAN
Pages
255–265
Publication Date
2022-10-19
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results showed that t-test and U-test give different results when the data showed skewness.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Checking the normality assumption is necessary to decide whether a parametric or non-parametric test needs to be used.
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Key Findings & Data:Different ways are suggested in literature to use for checking normality.
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Conclusion & Impact:The results showed that t-test and U-test give different results when the data showed skewness.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Normality test · Skewness · Mean comparison

Abstract

Checking the normality assumption is necessary to decide whether a parametric or non-parametric test needs to be used. Different ways are suggested in literature to use for checking normality. Skewness and kurtosis values are one of them. However, there is no consensus which values indicated a normal distribution. Therefore, the effects of different criteria in terms of skewness values were simulated in this study. Specifically, the results of t-test and U-test are compared under different skewness values. The results showed that t-test and U-test give different results when the data showed skewness. Based on the results, using skewness values alone to decide about normality of a dataset may not be enough. Therefore, the use of non-parametric tests might be inevitable.

Özet

Checking the normality assumption is necessary to decide whether a parametric or non-parametric test needs to be used. Different ways are suggested in literature to use for checking normality. Skewness and kurtosis values are one of them. However, there is no consensus which values indicated a normal distribution. Therefore, the effects of different criteria in terms of skewness values were simulated in this study. Specifically, the results of t-test and U-test are compared under different skewness values. The results showed that t-test and U-test give different results when the data showed skewness. Based on the results, using skewness values alone to decide about normality of a dataset may not be enough. Therefore, the use of non-parametric tests might be inevitable.

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Cite This Article

ORÇAN, F. (2022). Parametric or Non-parametric: Skewness to Test Normality for Mean Comparison. International Journal of Assessment Tools in Education, Cilt: 7 - Sayı: 2, 255–265. https://ajindex.com/articles/540144

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