Negatively Skewed Distribution
In statistics, a negatively skewed distribution, also known as a left-skewed distribution, is a type of distribution where the tail on the left side of the distribution is longer or fatter than the tail on the right side. This means that the majority of the data points are concentrated towards the right side of the distribution, while the extreme values are located in the left tail.

Extreme Values
Extreme values, also known as outliers, are observations that are significantly different from the other values in a dataset. In a negatively skewed distribution, the extreme values are located in the left tail of the distribution.

Explanation
To understand why the extreme values in a negatively skewed distribution lie in the left tail, let's consider a hypothetical example. Suppose we have a dataset of exam scores ranging from 0 to 100, where most students scored between 70 and 90, but a few students scored very low (e.g., 20 or 30).

In this scenario, the distribution of exam scores would be negatively skewed because the tail on the left side (representing low scores) would be longer or fatter than the tail on the right side. The majority of students would fall within the range of 70 to 90, which is towards the right side of the distribution. However, the few students who scored very low (the extreme values) would be located in the left tail of the distribution.

The reason for this lies in the definition of skewness. Skewness measures the asymmetry of a distribution. In a negatively skewed distribution, the mean is less than the median, indicating that the tail on the left side is longer. This means that there are more extreme values in the left tail than in the right tail.

Therefore, the extreme values in a negatively skewed distribution lie in the left tail because the tail on the left side is longer or fatter, indicating a higher concentration of extreme values in that region.