Skewness is a measure of what aspect of data distribution?

Skewness specifically measures the asymmetry of a probability distribution. When analyzing a dataset, skewness provides insight into whether the data points are distributed more heavily to one side of the average than the other. A distribution can be positively skewed, indicating that there are a larger number of data points on the left side of the distribution and a few unusually high values pulling the tail to the right. Conversely, a negatively skewed distribution has more data points on the right side with a few low values extending the tail to the left.

Understanding skewness is essential in data analysis because it helps to identify how the average might be affected by extreme values, which can influence interpretation and statistical findings. This characteristic of the distribution is critical, particularly when assessing normality, as many statistical tests assume that data distributions are symmetric.