Which term identifies a measure of how much scores in a dataset vary around the mean?

The term that identifies a measure of how much scores in a dataset vary around the mean is standard deviation. Standard deviation quantifies the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean of the dataset, while a high standard deviation indicates that the values are spread out over a wider range.

In a practical sense, standard deviation is particularly useful in understanding the distribution of data points and assessing the degree of variability within a dataset. It is calculated as the square root of the variance, which also measures variability but is expressed in squared units. Therefore, standard deviation provides a more intuitive measure of variability that is in the same units as the original data.

Other terms like variance, standard error, and range have distinct definitions and applications. Variance measures the average squared deviation from the mean, while standard error pertains to the precision of the sample mean estimate relative to the population mean. The range simply calculates the difference between the highest and lowest values in a dataset, offering a limited perspective on variability. Overall, standard deviation stands out as the most precise descriptor of how scores distribute around the mean.