Computes a Monte Carlo p-value (250,000 replications) for the null hypothesis that the sample data come from a normal distribution. If the p-value is small (e.g., less than .05) and the skewness estimate is positive, then the normality assumption can be rejected due to positive skewness. If the p-value is small (e.g., less than .05) and the skewness estimate is negative, then the normality assumption can be rejected due to negative skewness.
test.skew(y)vector of quantitative scores
Returns a 1-row matrix. The columns are:
Skewness - estimate of skewness coefficient
p - Monte Carlo two-sided p-value
y <- c(30, 20, 15, 10, 10, 60, 20, 25, 20, 30, 10, 5, 50, 40, 95)
test.skew(y)
#> Skewness p
#> 1.5201 0.0072
# Should return:
# Skewness p
# 1.5201 0.0067