What does skewness measure in a dataset?

Skewness is a statistical measure that assesses the symmetry of a probability distribution around its mean. When a dataset is perfectly symmetrical, the skewness is zero, indicating that the data values are evenly distributed around the mean. A positive skewness suggests that the tail on the right side of the distribution is longer or fatter than the left side, while a negative skewness indicates a longer or fatter tail on the left side.

Understanding skewness is crucial for interpreting the nature of the data's distribution, as it can impact the results of statistical analyses and interpretations. For instance, if skewness is significant, it may suggest that transformation of the data is needed to meet the assumptions of certain statistical tests that require normally distributed data. This makes recognizing and understanding skewness essential in business statistics, as it informs decisions based on data analysis.