Which measure indicates how spread out the values of a dataset are?

The measure that indicates how spread out the values of a dataset are is a measure of dispersion. Measures of dispersion provide insights into the variability or distribution of data points in relation to the mean or median of the dataset. Common examples of measures of dispersion include range, variance, and standard deviation.

By using measures of dispersion, analysts can determine the extent of variation within the dataset, which helps in understanding the data's overall characteristics. For instance, a high standard deviation indicates that the data points tend to be spread out over a wider range of values, while a low standard deviation suggests that they are clustered closely around the mean.

In contrast, confidence intervals provide a range within which we expect a population parameter to lie, but they do not directly measure data spread. Measures of central tendency, such as mean, median, and mode, focus on determining a central value of the dataset rather than its spread. The Z-score standardizes data points by indicating how many standard deviations a data point is from the mean, but again, it serves more to assess position rather than dispersion itself. Thus, measures of dispersion are the most accurate term for indicating how values in a dataset vary.