Define "skewness" in the context of probability distributions.

Skewness is a statistical term that describes the asymmetry of a probability distribution. When a distribution is skewed, it means that the data points do not cluster symmetrically around the mean. Instead, they tend to fall more on one side of the distribution.

A distribution can be positively skewed, where the tail on the right side is longer or fatter, indicating that the majority of the data points are concentrated on the left. Conversely, it can be negatively skewed, where the left tail is longer or fatter, suggesting concentration towards the right.

Understanding skewness is essential because it provides insights into the nature of the data, influencing the choice of statistical analyses and interpretations. This characteristic can have significant implications in various fields, including psychology, where the shape of the distribution can inform researchers about underlying behaviors or phenomena.

The other choices do not accurately define skewness: uniformity refers to the equality of data points, the number of data points pertains to frequency rather than distribution shape, and spread relates to variance or standard deviation rather than asymmetry.