Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/153945
Title: Skewness and Kurtosis in real data samples
Author: Blanca, María J.
Arnau, Jaume
López Montiel, Dolores
Bono Cabré, Roser
Bendayan, Rebecca
Keywords: Estadística
Distribució (Teoria de la probabilitat)
Statistics
Distribution (Probability theory)
Issue Date: 2013
Publisher: European Association of Methodology
Abstract: Parametric statistics are based on the assumption of normality. Recent findings suggest that Type I error and power can be adversely affected when data are non-normal. This paper aims to assess the distributional shape of real data by examining the values of the third and fourth central moments as a measurement of skewness and kurtosis in small samples. The analysis concerned 693 distributions with a sample size ranging from 10 to 30. Measures of cognitive ability and of other psychological variables were included. The results showed that skewness ranged between −2.49 and 2.33. The values of kurtosis ranged between −1.92 and 7.41. Considering skewness and kurtosis together the results indicated that only 5.5% of distributions were close to expected values under normality. Although extreme contamination does not seem to be very frequent, the findings are consistent with previous research suggesting that normality is not the rule with real data.
Note: Versió postprint del document publicat a: https://doi.org/10.1027/1614-2241/a000057
It is part of: Methodology. European Journal of Research Methods tor the Behavioral and Social Sciences, 2013, num. 9, p. 78-84
URI: http://hdl.handle.net/2445/153945
Related resource: https://doi.org/10.1027/1614-2241/a000057
ISSN: 1614-1881
Appears in Collections:Articles publicats en revistes (Psicologia Social i Psicologia Quantitativa)

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