skewness: Skewness test

In normality: Tests for Departure from Normality

Skewness test

Description

The test evaluates whether the population skewness is equal to zero. Under the null hypothesis, the data are assumed to originate from a symmetric distribution. Significant positive or negative skewness indicates asymmetry in the distribution and may suggest a departure from normality.

Usage

skewness(
  x,
  alpha = 0.05,
  alternative = c("two.sided", "less", "greater"),
  method = c("G1", "b1", "g1"),
  silent = FALSE,
  summary = TRUE
)

Arguments

x	Numeric vector containing the input data.
alpha	Numeric (default: 0.05). Significance level for hypothesis testing. Must be between 0 and 1.
alternative	Character (default: "two.sided"). Specifies the alternative hypothesis. Available options are c("two.sided", "less", "greater").
method	Character (default: "G1"). Formula used to estimate skewness. Available options are c("G1", "b1", "g1"). The "g1" statistic is the conventional moment-based sample skewness. The "G1" and "b1" statistics apply finite-sample corrections to reduce the bias of "g1".
silent	Logical (default: FALSE). If FALSE, the test results are printed to the console.
summary	Logical (default: TRUE). Produce a summary table.

Value

A list

References

Joanes, D.N., Gill, C.A., 1998. Comparing measures of sample skewness and kurtosis. J. R. Stat. Soc. D (The Statistician) 47, 183–189. https://doi.org/10.1111/1467-9884.00122

Wright, D.B., Herrington, J.A., 2011. Problematic standard errors and confidence intervals for skewness and kurtosis. Behav. Res. Methods 43, 8–17. https://doi.org/10.3758/s13428-010-0044-x

Examples

skewness(rnorm(30))

normality documentation built on Aug. 23, 2026, 1:07 a.m.