Which term describes a distribution of data where scores cluster towards one end of the range rather than being evenly distributed?

A distribution of data where scores cluster towards one end of the range, creating a lack of symmetry, is referred to as a skewed distribution. This type of distribution can take on two forms—positive skew, where the tail of the distribution extends towards the higher values, and negative skew, where the tail extends towards the lower values.

In skewed distributions, the mean is influenced by the extreme scores in the tail, leading to a situation where the mean, median, and mode may differ significantly. Understanding this concept is essential because it reflects how data is not evenly spread out and can reveal trends or outliers that are not immediately apparent in more symmetrical distributions.

In contrast, a normal distribution is characterized by its bell-shaped curve where data points are symmetrically distributed around the mean, and a uniform distribution has all scores equally represented throughout the range. A bi-modal distribution, on the other hand, has two different modes or peaks, indicating that two different groups or phenomena may be present within the data. Thus, the term that accurately describes the clustering of data towards one end of the range is indeed skewed distribution.