What does skewness measure in a distribution?

Skewness quantifies the asymmetry of a probability distribution. When a distribution is perfectly symmetrical, it has a skewness of zero. However, in most real-world data, you will often find distributions that are not symmetrical; this is where skewness comes into play. A positive skewness indicates that the tail on the right side of the distribution is longer or fatter than the left side, while a negative skewness indicates that the tail on the left side is longer or fatter than the right side.

Understanding skewness is essential as it provides insights into the nature of the data distribution and can influence the choice of statistical analyses, as certain methods assume normality (symmetry). Recognizing skewness is crucial for interpreting the shape of the data’s distribution and making informed decisions in data analysis.

The other options provided do not accurately capture the specific role of skewness, as they pertain to different concepts such as symmetry, central tendency, and variability, which are separate metrics used in the field of statistics.