What is a distribution called when scores tend to cluster more towards one end of the range?

A distribution where scores cluster more towards one end of the range is referred to as a skewed distribution. In this type of distribution, the data is not evenly spread out; instead, it shows a tendency for the values to lean towards either the lower or upper end. This results in one tail being longer than the other, indicating that there are either a few high values (right skew) or few low values (left skew).

Understanding skewness is important in psychology and other fields because it affects how we interpret data. For instance, if we have a right-skewed distribution, the average might be pulled up by a few high scores, which could misrepresent the typical score in a dataset. In contrast, normal distributions are symmetrical with the mean, median, and mode all aligning at the center, while uniform distributions show equal frequency across all values. Bimodal distributions contain two distinct peaks or modes, indicating the presence of two different groups within the data.