Skewness vs Outlier

A distribution is skewed when values trail off farther on one side than the other. In a right-skewed distribution the long tail points toward high values, which pulls the mean above the median; in a left-skewed distribution the reverse happens. For example, incomes are often right-skewed because a few very high earners stretch the upper tail. A useful rule of thumb is that the mean gets dragged toward the longer tail.

An outlier is a point that stands apart from the overall pattern of the data. A common rule flags a value as an outlier when it falls below $Q_1 - 1.5 \times \text{IQR}$ or above $Q_3 + 1.5 \times \text{IQR}$, using the first quartile $Q_1$, the third quartile $Q_3$, and the interquartile range. For example, with $Q_1 = 3$, $Q_3 = 9$, and IQR $= 6$, any value below $3 - 9 = -6$ or above $9 + 9 = 18$ is an outlier. Outliers can be genuine extreme cases or data-entry errors, so you investigate before removing them.