Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis

Hae-Young Kim

doi:10.5395/rde.2013.38.1.52

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Open Lecture on Statistics Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis: Hae-Young Kim; 2013;38(1):-54.
DOI: https://doi.org/10.5395/rde.2013.38.1.52
Published online: February 26, 2013

Department of Dental Laboratory Science & Engineering, Korea University College of Health Science, Seoul, Korea.

Correspondence to Hae-Young Kim, DDS, PhD. Associate Professor, Department of Dental Laboratory Science & Engineering, Korea University College of Health Science, San 1 Jeongneung 3-dong, Seongbuk-gu, Seoul, Korea 136-703. TEL, +82-2-940-2845; FAX, +82-2-909-3502, kimhaey@korea.ac.kr

©Copyights 2013. The Korean Academy of Conservative Dentistry.

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1) Skewness and kurtosis
2) Normality test using skewness and kurtosis
3) How strict is the assumption of normality?
REFERENCES

As discussed in the previous statistical notes, although many statistical methods have been proposed to test normality of data in various ways, there is no current gold standard method. The eyeball test may be useful for medium to large sized (e.g., n > 50) samples, however may not useful for small samples. The formal normality tests including Shapiro-Wilk test and Kolmogorov-Smirnov test may be used from small to medium sized samples (e.g., n < 300), but may be unreliable for large samples. Moreover we may be confused because 'eyeball test' and 'formal normality test' may show incompatible results for the same data. To resolve the problem, another method of assessing normality using skewness and kurtosis of the distribution may be used, which may be relatively correct in both small samples and large samples.

1) Skewness and kurtosis

Skewness is a measure of the asymmetry and kurtosis is a measure of 'peakedness' of a distribution. Most statistical packages give you values of skewness and kurtosis as well as their standard errors.

In SPSS you can find information needed under the following menu: Analysis - Descriptive Statistics - Explore

Skewness is a measure of the asymmetry of the distribution of a variable. The skew value of a normal distribution is zero, usually implying symmetric distribution. A positive skew value indicates that the tail on the right side of the distribution is longer than the left side and the bulk of the values lie to the left of the mean. In contrast, a negative skew value indicates that the tail on the left side of the distribution is longer than the right side and the bulk of the values lie to the right of the mean. West et al. (1996) proposed a reference of substantial departure from normality as an absolute skew value > 2.1

Kurtosis is a measure of the peakedness of a distribution. The original kurtosis value is sometimes called kurtosis (proper) and West et al. (1996) proposed a reference of substantial departure from normality as an absolute kurtosis (proper) value > 7.1 For some practical reasons, most statistical packages such as SPSS provide 'excess' kurtosis obtained by subtracting 3 from the kurtosis (proper). The excess kurtosis should be zero for a perfectly normal distribution. Distributions with positive excess kurtosis are called leptokurtic distribution meaning high peak, and distributions with negative excess kurtosis are called platykurtic distribution meaning flat-topped curve.

2) Normality test using skewness and kurtosis

A z-test is applied for normality test using skewness and kurtosis. A z-score could be obtained by dividing the skew values or excess kurtosis by their standard errors.

As the standard errors get smaller when the sample size increases, z-tests under null hypothesis of normal distribution tend to be easily rejected in large samples with distribution which may not substantially differ from normality, while in small samples null hypothesis of normality tends to be more easily accepted than necessary. Therefore, critical values for rejecting the null hypothesis need to be different according to the sample size as follows:

For small samples (n < 50), if absolute z-scores for either skewness or kurtosis are larger than 1.96, which corresponds with a alpha level 0.05, then reject the null hypothesis and conclude the distribution of the sample is non-normal.
For medium-sized samples (50 < n < 300), reject the null hypothesis at absolute z-value over 3.29, which corresponds with a alpha level 0.05, and conclude the distribution of the sample is non-normal.
For sample sizes greater than 300, depend on the histograms and the absolute values of skewness and kurtosis without considering z-values. Either an absolute skew value larger than 2 or an absolute kurtosis (proper) larger than 7 may be used as reference values for determining substantial non-normality.

Referring to Table 1 and Figure 1, we could conclude all the data seem to satisfy the assumption of normality despite that the histogram of the smallest-sized sample doesn't appear as a symmetrical bell shape and the formal normality tests for the largest-sized sample were rejected against the normality null hypothesis.

Figure 1

Histograms of a characteristic of interests in various sizes of samples.

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Table 1

Skewness, kurtosis and normality tests for a characteristic of interests in various sizes of samples

^*Lilliefors significance correct

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3) How strict is the assumption of normality?

Though the humble t test (assuming equal variances) and analysis of variance (ANOVA) with balanced sample sizes are said to be 'robust' to moderate departure from normality, generally it is not preferable to rely on the feature and to omit data evaluation procedure. A combination of visual inspection, assessment using skewness and kurtosis, and formal normality tests can be used to assess whether assumption of normality is acceptable or not. When we consider the data show substantial departure from normality, we may either transform the data, e.g., transformation by taking logarithms, or select a nonparametric method such that normality assumption is not required.

REFERENCES

1. West SG, Finch JF, Curran PJ. In: Hoyle RH, editor. Structural equation models with nonnormal variables: problems and remedies. Structural equation modeling: Concepts, issues and applications. 1995. Newbery Park, CA: Sage; p. 56-75.

Tables & Figures

Figure 1

Histograms of a characteristic of interests in various sizes of samples.

Download Figure

Table 1

Skewness, kurtosis and normality tests for a characteristic of interests in various sizes of samples

^*Lilliefors significance correct

Download Table

REFERENCES

1. West SG, Finch JF, Curran PJ. In: Hoyle RH, editor. Structural equation models with nonnormal variables: problems and remedies. Structural equation modeling: Concepts, issues and applications. 1995. Newbery Park, CA: Sage; p. 56-75.

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Journal of Psychosomatic Research.2020; 136: 110178. CrossRef
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J. Lydia Pramitha, A. John Joel, Srisaila Srinivas, R. Sreeja, Firoz Hossain, R. Ravikesavan
Physiology and Molecular Biology of Plants.2020; 26(2): 353. CrossRef
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Impact of Parents’ Knowledge about the Development of Self-Esteem in Adolescents and Their Parenting Practice on the Self-Esteem and Suicidal Behavior of Urban High School Students in Nepal
Ratna Shila Banstola, Tetsuya Ogino, Sachiko Inoue
International Journal of Environmental Research and Public Health.2020; 17(17): 6039. CrossRef
Profiles of adolescent leisure motivation and substance use in the HealthWise South Africa research trial: a person-centered approach
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Quality of Life in Heterogeneous Anxiety Disorders: Changes Across Cognitive-Behavioral Treatments
Julianne G. Wilner, Steven Dufour, Katherine Kennedy, Shannon Sauer-Zavala, Hannah Boettcher, Todd J. Farchione, David H. Barlow
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Psychometric analyses of the Bangla version of the Dark Triad Dirty Dozen
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Risk Perception on Haze Pollution and Willingness to Pay for Self-Protection and Haze Management in Chiang Mai Province, Northern Thailand
Sukanya Sereenonchai, Noppol Arunrat, Duangporn Kamnoonwatana
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Synergistic Effect of Low Neutrophil–Lymphocyte Ratio With Physical Activity on Quality of Life in Type 2 Diabetes Mellitus: A Community-Based Study
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Journal of Construction Engineering and Management.2020;[Epub] CrossRef
Validation of the care providers version of the Rainbow Model of Integrated Care-measurement tool in Chinese primary care systems
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Chronobiology International.2020; 37(12): 1736. CrossRef
Norm values and psychometric properties for the German health regulatory focus scale – results of a representative survey
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BMC Medical Research Methodology.2020;[Epub] CrossRef
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D W Fointuna, A H Kaluge, A J Fernandez
Journal of Physics: Conference Series.2020; 1422(1): 012025. CrossRef
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Klaudia Krenca, Eliane Segers, Xi Chen, Sharry Shakory, Jeffrey Steele, Ludo Verhoeven
Reading and Writing.2020; 33(2): 267. CrossRef
The impact of food neophobia and sensation seeking of foreign tourists on the purchase intention of traditional Turkish food
Kadri Koray Sivrikaya, Yeliz Pekerşen
International Journal of Gastronomy and Food Science.2020; 21: 100222. CrossRef
Physiological changes during prolonged standing and walking considering age, gender and standing work experience
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Ergonomics.2020; 63(5): 579. CrossRef
Validation of a translated internet gaming disorder scale (short form) and measurement invariance across sex groups in Malaysian samples
Soo Ting T’ng, Kee Pau
Current Psychology.2020; 39(6): 1977. CrossRef
Sex differences in muscle activity and motor variability in response to a non-fatiguing repetitive screwing task
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Biology of Sex Differences.2020;[Epub] CrossRef
Herding in human groups is related to high autistic traits
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Increased and More Heterogeneous Gadoxetic Acid Uptake of the Liver Parenchyma after Hepatitis C Virus Eradication by Direct Antiviral Agent
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Michelle Arnold, Brent J. Small, Kathryn Hyer, Theresa Chisolm, Melissa T. Frederick, ShienPei C. Silverman, Gabrielle H. Saunders
International Journal of Audiology.2019; 58(5): 287. CrossRef
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International Journal of Mental Health and Addiction.2019; 17(4): 935. CrossRef
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Journal of Medical Internet Research.2019; 21(6): e11188. CrossRef
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Child Indicators Research.2018; 11(4): 1201. CrossRef
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Archives of Sexual Behavior.2018; 47(6): 1565. CrossRef
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Work stress, anthropometry, lung function, blood pressure, and blood-based biomarkers: a cross-sectional study of 43,593 French men and women
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Long-term acoustic monitoring of fish calling provides baseline estimates of reproductive timelines in the May River estuary, southeastern USA
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Topiramate and accommodation: Does topiramate cause accommodative dysfunction?
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Canadian Journal of Ophthalmology.2017; 52(1): 20. CrossRef
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Stress-induced change in serum BDNF is related to quantitative family history of alcohol use disorder and age at first alcohol use
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Long‐term reliability and stability of behavioral measures among adolescents: The Delay Discounting and Stroop tasks
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COMPARISON OF FEMALE AND MALE YOUTH CHARACTERISTICS PRIOR TO ENTREPRENEURIAL DEVELOPMENT: EVIDENCE FROM UGANDA
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Fun Seeking and Reward Responsiveness Moderate the Effect of the Behavioural Inhibition System on Coping-Motivated Problem Gambling
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Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis

Restor Dent Endod. 2013;38(1):52-54. Published online February 26, 2013

DOI: https://doi.org/10.5395/rde.2013.38.1.52

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Figure

Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis

Figure 1 Histograms of a characteristic of interests in various sizes of samples.

Figure 1

Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis

Table 1

Skewness, kurtosis and normality tests for a characteristic of interests in various sizes of samples

^*Lilliefors significance correct

Table 1 Skewness, kurtosis and normality tests for a characteristic of interests in various sizes of samples

^*Lilliefors significance correct