Skewness as described above has been defined in different ways by various authors. Some important definitions are quoted as under:
- According to Croxton and Cowden, “When a series is not symmetrical, it is said to be asymmetrical, or skewed.”
- According to Garret, “A distribution is said to be skewed when the Mean and the Median fall at different points in the distribution and balance (or the centre of gravity) is shifted to one side or the other to left or right”.
- According to Simpson and Kafka, “Measures of skewness tell us the direction and the extent of skewness. In symmetrical distribution, the Mean, Median and Mode are identical. The more the Mean moves away from the Mode, the larger the asymmetry or skewness”.
From the above definitions, the characteristics of skewness may be outlined as under:
- 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.