In statistics, skewness is a measure of the asymmetry of the distribution of a random variable around its mean. If a distribution has a positive skew, it means that it has a longer tail on the right side of the distribution, while a negative skew indicates a longer tail on the left side. If a distribution has a skewness of zero, it means that it is perfectly symmetrical.
Skewness is an important concept in statistics because it can affect the validity of certain statistical tests and models. For example, if the data is highly skewed, it may not be appropriate to use a parametric statistical test that assumes a normal distribution.
Skewness can be calculated using a variety of methods, including the sample skewness formula, which involves calculating the coefficient of the third standardized moment of the distribution. Other methods include the Pearson mode skewness and the quartile skewness coefficient.
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