Mardia's multivariate skewness and kurtosis tests generalize the moment tests to the multivariate case. The mean, variance, skewness, and kurtosis of the skewed Cauchy distribution are all undefined. Mardia's test is based on multivariate extensions of skewness and kurtosis measures. These quantities consistently estimate the theoretical skewness and kurtosis of the distribution, respectively. The variance, skewness, and excess kurtosis can be calculated from these raw moments. In a subsequent paper Pearson reported that for any distribution skewness + 1 < kurtosis. D'Agostino's K-squared test is a goodness-of-fit normality test based on sample skewness and sample kurtosis. This includes identifying outliers, skewness, kurtosis, a need for transformations, and mixtures. Alternatively, rules of thumb based on the sample skewness and kurtosis have also been proposed. It can be seen as the median of all possible quantile skewness measures. Moreover, the skewness and coefficient of variation depend only on the shape parameter. This is closely related in form to Pearson's second skewness coefficient. Examples of distributions with finite skewness include the following. Large skewness compromises the accuracy of the interpolated regions. They rely on the medcouple statistic of skewness.