What term describes a distribution of scores that is not symmetrical, often due to outliers?

A skewed distribution is defined as a distribution that is not symmetrical, indicating that one tail of the distribution is longer or fatter than the other. This asymmetry often arises due to outliers, which are extreme values that can disproportionately affect the mean and pull the distribution in one direction.

For instance, if a dataset contains a small number of very high scores, the distribution will be skewed to the right (positively skewed), suggesting that most scores are on the lower end. Conversely, if the dataset has a few very low scores, it would be skewed to the left (negatively skewed). Understanding skewness is crucial for interpreting data, as it highlights the impact of outliers and influences statistical analyses, such as the choice between using the mean or median to represent central tendency.

The other distribution types do not exhibit this kind of asymmetry. A normal distribution is perfectly symmetrical around the mean, while a bimodal distribution contains two different peaks and a uniform distribution describes a situation where all outcomes are equally likely, resulting in a flat, even distribution. These characteristics distinguish skewed distributions and underscore their importance in data analysis.