Skewness measures how asymmetric (uneven) a distribution is around its average. It tells you whether the data values are spread out more to one side (tail) than the other.

• Zero skewness → distribution is symmetrical (balanced left and right).
• Positive skewness → right tail is longer (more small values, few large ones).
• Negative skewness → left tail is longer (more large values, few small ones).

Simple Example:
Suppose test scores:
20, 30, 35, 40, 90
Most students have lower scores, and only one has a very high score → positive skewness (tail on right).

Another set:
10, 60, 65, 70, 75
Only one low score → negative skewness (tail on left).

What it tells you: Skewness helps identify if extreme values (outliers) are mostly on one end of the data.