How is a skewed distribution characterized?

A skewed distribution is characterized by its asymmetrical nature, where one tail of the distribution is longer or fatter than the other. This means that the data does not mirror itself around a central point. In a skewed distribution, one side contains more extreme values than the other, which can significantly affect the mean and median of the data set.

For instance, in a right-skewed distribution, a larger number of lower values pulls the mean to the right, while in a left-skewed distribution, higher values create a longer left tail. This asymmetry is crucial in understanding the data’s behavior and impacts statistical analyses, making the identification of skewness an important aspect of data interpretation in evidence-informed practice.