SKEWNESS

Skewness

                  Skewness describes how much statistical data distribution is asymmetrical from the normal distribution, where distribution is equally divided on each side. If a distribution is not symmetrical or Normal, then it is skewed, that is, it is either the frequency distribution skewed to the left side or to the right side.

Characteristics

There are several characteristics of skewness in statistics they are,

 It refers to the asymmetry of a statistical series.

 It refers to the difference in values of the averages viz. Mean, Median and Mode.

 It refers to the difference in distance between the Quartiles, and the Median.

 It may be positive, or negative. If there is more concentration in lower values it is 

positive, and if  there is more concentration in higher values it is negative.

Types of Skewness

            If the distribution is symmetric, then it has a skewness of 0 & it’s Mean = Median =Mode. So basically, there are two types –

Positive Skewness

        The distribution is positively skewed when most of the frequency of distribution lies on the right side of distribution and has a longer and fatter right tail where the distribution’s Mean > Median > Mode.

Negative Skewness

       The distribution is negatively skewed when most of the frequency of distribution lies on the left side of distribution and has a longer and fatter left tail where the distribution’s Mean <Median  < Mode.