Understanding Negative Skew: Exploring the Asymmetry of Data Distributions

Negative skew

Negative skew, also known as left skew, is a term used in statistics to describe the shape of a distribution of data

Negative skew, also known as left skew, is a term used in statistics to describe the shape of a distribution of data. Skewness is a measure of the asymmetry of a distribution.

A negatively skewed distribution is one in which the tail on the left side of the distribution is longer or fatter than the tail on the right side. This means that there are more data points with lower values and fewer data points with higher values.

In a negatively skewed distribution, the mean is less than the median and the mode is greater than the median. The mean is pulled towards the lower end of the distribution by the few very low values, making it lower than the median. The mode, which is the most frequently occurring value, tends to be higher than the median because of the concentration of lower values on the left side of the distribution.

One common example of a negatively skewed distribution is the distribution of income. In many countries, there are a few individuals with extremely high incomes, which cause the mean income to be higher than the median income. However, the majority of individuals have lower incomes, leading to a longer tail on the left side of the distribution.

To identify negative skew in a dataset, you can plot a histogram or a frequency polygon. If the tail of the distribution extends further to the left than the right, it is negatively skewed.

It’s important to note that skewness does not imply any causation or reasoning behind the distribution. It is simply a measure of the shape of the data. Negative skewness indicates that there is more concentration of data towards the higher end of the distribution.

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