• Skewing is asymmetry in a statistical distribution, in which the curve appears distorted (or) skewed either to the left (or) to the right.
  • It can be quantified to define the extent to which a distribution differs from a normal distribution.
  • In normal distribution, the graph appears as a classical, symmetrical "bell-shaped curve". The mean and the mode are equal.
        
  • When a distribution is skewed to the left, the tail on the curve's left-hand side is longer than the tail on the right-hand side, and the mean is less than the mode. This situation is called negative skewness.
  • When a distribution is skewed to the right, the tail on the curve's right-hand side is longer than the tail on the left-hand side, and the mean is greater than the mode. this situation is called positive skewness.
       For example,
  • Suppose in a neighborhood with 100 homes, 99 of them sell for $100,000, and one sells for $1,000,000. The median and the mode will be $1,00,000, but the mean will be $109,000. Hence, the mean has been "pulled" upward(to the right) by the existence of one home in the neighborhood.
Positive Skew

  • If a school test is easy, then most of the students will perform well in it. This maximum bulk of students will take up the right side of the negatively skewed distribution curve. On the contrary, a few students may perform poorly, and even get very low marks in the test. These extreme values of low magnitude (less marks) extends the tail in the negative or left direction from the distribution, making it a negatively skewed distribution. Here, there is a high frequency of high scores and low frequency of less scores.
       Negative Skew