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Dergiler / International Journal of Assessment Tools in Education / 2026 / Volume: 7 Issue: 2

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

Fatih Orcan(Trabzon Üniversitesi)0000-0003-1727-0456
Sayfa
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Yayın Tarihi
2020-06-13
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Therefore, the use of non-parametric tests might be inevitable.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Checking the normality assumption is necessary to decide whether a parametric or non-parametric test needs to be used.
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Temel Bulgular & Veri:Different ways are suggested in literature to use for checking normality.
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Sonuç & Katkı:Therefore, the use of non-parametric tests might be inevitable.
🔬Tür:Research Article
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Normality test · Skewness · Mean comparison

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.

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.

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Bu Makaleye Atıf Yap

Orcan, F. (2020). Parametric or Non-parametric: Skewness to Test Normality for Mean Comparison. International Journal of Assessment Tools in Education, Volume: 7 Issue: 2. https://doi.org/10.21449/ijate.656077

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