3.13:
Types of Skewness
Recall that in the distribution of a dataset, if the left half of the graph is not a mirror image of the right half, the data is said to be skewed.
There are three types of skewness. If a graph extends to the left side forming a longer tail on the left, it is called negatively skewed. If a graph extends to the right side forming a longer tail on the right, it is said to be positively skewed. Finally, a graph with symmetric or normal distribution has zero skewness.
A graph representing negatively skewed data usually has the mean and median to the left side of the mode.
Conversely, a positively skewed dataset has the mean and median to the right side of the mode.
For example, the distribution of annual income among residents of a city, a large number of people on the lower-income side, indicates positive skewness. Whereas, the distribution of students’ scores on an easy exam, with fewer students scoring lower scores, shows negatively skewed data.
3.13:
Types of Skewness
If the frequency distribution of a data set is more inclined towards smaller or larger values, the distribution is said to be skewed. If data values are skewed to the right, then the distribution is called positively skewed. Conversely, if the plot is skewed to the left, the distribution is called negatively skewed.
For instance, in the middle of a pandemic, the geographical distribution of vaccine coverage may be positively skewed towards populations in the global north countries. However, within any one such country, the vaccine distribution may be negatively skewed towards the percentage of the population, indicating that a significant fraction of the country’s population is vaccinated.
In the case of a skewed distribution, the mean and median also lie more decidedly towards the direction of the skew. However, by definition, the mode peaks at the distribution’s peak.