What term measures how far, on average, scores differ from the mean?

The correct answer is the term that refers to the average distance of each score from the mean within a data set, which is known as standard deviation. This statistical measure provides insight into how spread out the scores are in relation to the mean. A small standard deviation indicates that the scores are clustered closely around the mean, while a large standard deviation suggests a greater dispersion of scores.

Standard deviation is particularly valuable because it is expressed in the same units as the original data, making it easier to interpret and apply in statistical analysis. It serves as a cornerstone for many statistical applications, including inferential statistics, allowing researchers to draw conclusions about the data's reliability and variability.

Other terms, while related to the concept of variability, serve different purposes. For instance, standard error refers to the estimation of the standard deviation of the sampling distribution of a statistic, rather than the data itself. Range measures the difference between the highest and lowest values in a dataset but does not provide information about how scores spread around the mean. Variance, on the other hand, is the average of the squared differences from the mean, and while it indicates variance in scores, it is expressed in squared units, which can make interpretation more complex compared to standard deviation.