Negative skew
In statistics, skewness is a measure of the asymmetry of a probability distribution
In statistics, skewness is a measure of the asymmetry of a probability distribution. It tells us whether the data is concentrated more towards the left or the right of the distribution.
When we say a distribution has a negative skew, it means that the tail on the left side of the distribution is longer or fatter than the tail on the right side. In other words, the data points are concentrated towards the right side of the distribution, while the left side is stretched out.
To visualize a negative skew, imagine a dataset that represents the heights of people in a population. If the majority of people are of average height, but there are a few extremely short individuals, the distribution would have a negative skew. The tail on the left side would represent the shorter individuals, while the majority of people would be concentrated towards the right side.
A negative skew can also be seen in financial data, such as stock returns. If most of the returns are small or negative, but there are a few extremely high positive returns, the distribution would have a negative skew. The tail on the left side would represent the negative returns, while the positive returns would be concentrated towards the right side.
In terms of the mean, median, and mode, a negatively skewed distribution will have the mean < median < mode. The mean is pulled towards the left side of the distribution due to the tail. The median represents the midpoint, which would be closer to the right side because of the concentration of data, and the mode represents the highest point or peak of the distribution, which would be towards the right. It is important to note that a negative skew does not imply anything negative about the data or its distribution. It is simply a measure of asymmetry. Understanding the skewness of a distribution can help in interpreting and analyzing data accurately.
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