Final answer:
Skewness is the term used to describe the extent to which scores cluster on one side of the center of a distribution, providing an indication of the asymmetry of the data about the mean. The correct answer is C.
Step-by-step explanation:
The term that describes the extent to which scores cluster on one side of the center of a distribution is skewness. Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. The skewness can be negative, positive, or undefined. In a perfectly symmetrical distribution, the skewness is zero.
When data are skewed right (or positively skewed), the mean is typically greater than the median because the longer tail of the distribution pulls the mean toward the higher values. Conversely, when data are skewed left (or negatively skewed), the mean is usually less than the median, as the longer tail is on the lower end of the value range. For a symmetrical distribution, the mean, median, and mode are all located at the center, and thus, they are equal.
Understanding skewness is essential especially when working with non-symmetrical distributions, as it informs which measure of central tendency (mean, median, or mode) is the most appropriate for the data. If the data are skewed, often times, median is preferred over the mean, as it is more resistant to outliers and skewed data. When you graph your data, whether in a histogram or a box plot, skewness becomes more apparent, and this visual representation can significantly aid in data interpretation.