Which statistical concept's interpretation as a measure of dispersion relies on data being normally distributed?

The standard deviation is a statistical measure that quantifies the amount of variation or dispersion in a set of data values. Its interpretation is particularly meaningful when the data is normally distributed. In a normal distribution, approximately 68% of the data points fall within one standard deviation from the mean, about 95% within two standard deviations, and nearly all (99.7%) within three standard deviations. This relationship reflects how the standard deviation provides insights into the spread of data around the mean.

In contrast, while variance, which is the square of the standard deviation, also pertains to dispersion, its interpretation in terms of data spread is closely tied to the standard deviation itself rather than a standalone measure. The mean and median, on the other hand, are measures of central tendency rather than dispersion, focusing on the center of the data set rather than how the data varies around that center. Thus, the normal distribution's properties give the standard deviation a specific context that enhances its interpretation regarding dispersion.