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In statistics and probability theory, the nonparametric skew is a statistic occasionally used with random variables that take real values. It is a measure of the skewness of a random variable's distribution—that is, the distribution's tendency to "lean" to one side or the other of the mean. Its calculation does not require any knowledge of the form of the underlying distribution—hence the name nonparametric. It has some desirable properties: it is zero for any symmetric distribution; it is unaffected by a scale shift; and it reveals either left- or right-skewness equally well. In some statistical samples it has been shown to be less powerful than the usual measures of skewness in detecting departures of the population from normality.
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References
- ↑ Amaratunga, D.; Cabrera, J. (2001). "Analysis of Data from Viral DNA Microchips". Journal of the American Statistical Association. 96 (456): 1161. doi:10.1198/016214501753381814. S2CID 18154109.
- ↑ Bolstad, B. M.; Irizarry, R. A.; Astrand, M.; Speed, T. P. (2003). "A comparison of normalization methods for high density oligonucleotide array data based on variance and bias". Bioinformatics. 19 (2): 185–193. doi: 10.1093/bioinformatics/19.2.185 . PMID 12538238.