Data Skewness Measure

Context:
- It can (typically) be related to a Statistical Function.
- …
Counter-Example(s):
- Data Dispersion.
See: Data Transformation (Statistics)#Transforming to Normality, Probability Theory, Statistics, Probability Distribution, Real Number, Random Variable, Unimodal, Long Tail, Fat-Tailed Distribution, #Relationship of Mean And Median.

References

(Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/skewness Retrieved:2017-10-12.
- In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness value can be positive or negative, or undefined.
  The qualitative interpretation of the skew is complicated and unintuitive. Skew does not refer to the direction the curve appears to be leaning; in fact, the opposite is true. For a unimodal distribution, negative skew indicates that the tail on the left side of the probability density function is longer or fatter than the right side – it does not distinguish these two kinds of shape. Conversely, positive skew indicates that the tail on the right side is longer or fatter than the left side. In cases where one tail is long but the other tail is fat, skewness does not obey a simple rule. For example, a zero value means that the tails on both sides of the mean balance out overall; this is the case for a symmetric distribution, but is also true for an asymmetric distribution where the asymmetries even out, such as one tail being long but thin, and the other being short but fat. Further, in multimodal distributions and discrete distributions, skewness is also difficult to interpret. Importantly, the skewness does not determine the relationship of mean and median. In cases where it is necessary, data might be transformed to have a normal distribution.