In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed. Common examples of measures of statistical dispersion are the variance, standard deviation, and interquartile range. For instance, when the variance of data in a set is large, the data is widely scattered. On the other hand, when the variance is small, the data in the set is clustered.

Dispersion is contrasted with location or central tendency, and together they are the most used properties of distributions.

A measure of statistical dispersion is a nonnegative real number that is zero if all the data are the same and increases as the data become more diverse.

Most measures of dispersion have the same units as the quantity being measured. In other words, if the measurements are in metres or seconds, so is the measure of dispersion. Examples of dispersion measures include:

These are frequently used (together with scale factors) as estimators of scale parameters, in which capacity they are called estimates of scale. Robust measures of scale are those unaffected by a small number of outliers, and include the IQR and MAD.

All the above measures of statistical dispersion have the useful property that they are location-invariant and linear in scale. This means that if a random variable ${\displaystyle X}$ has a dispersion of ${\displaystyle S_{X}}$ then a linear transformation ${\displaystyle Y=aX+b}$ for real ${\displaystyle a}$ and ${\displaystyle b}$ should have dispersion ${\displaystyle S_{Y}=|a|S_{X}}$, where ${\displaystyle |a|}$ is the absolute value of ${\displaystyle a}$, that is, ignores a preceding negative sign ${\displaystyle -}$.

Other measures of dispersion are dimensionless. In other words, they have no units even if the variable itself has units. These include:

There are other measures of dispersion: