Skewness. The question arises in statistical analysis of deciding how skewed a distribution can be before it is considered a problem that is, non normality. One way of determining if the degree of skewness is "significantly skewed" is to compare the numerical value for "Skewness" with twice the "Standard Error of Skewness" and include the range from minus twice the Std. Error of Skewness to plus twice the Std. Error of Skewness. If the value for Skewness falls within this range, the skewness is considered not seriously violated.
Kurtosis. Another descriptive statistic that can be derived to describe a distribution is called kurtosis. A distribution is platykurtic if it is flatter than the corresponding normal curve and leptokurtic if it is more peaked than the normal curve.The same numerical process can be used to check if the kurtosis is significantly non normal. A normal distribution will have Kurtosis value of zero. So again we construct a range of "normality" by multiplying the Std. Error of Kurtosis by 2 and going from minus that value to plus that value.
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