The median absolute deviation is a measure of statistical dispersion.

Here "variability" could be quantified by the variance or any other measure of statistical dispersion.

The studentized range and the coefficient of variation are allowed to measure statistical dispersion.

Several measures of statistical dispersion are defined in terms of the absolute deviation.

Otherwise, the probability of duplicates could be significantly higher, since the statistical dispersion might be lower.

Various measures of statistical dispersion satisfy these.

This occurs frequently in estimation of scale parameters by measures of statistical dispersion.

The range is the size of the smallest interval which contains all the data and provides an indication of statistical dispersion.

Common examples of measures of statistical dispersion are the variance, standard deviation and interquartile range.

Statistics of the distribution of deviations are used as measures of statistical dispersion.

In a certain sense, the standard deviation is a "natural" measure of statistical dispersion if the center of the data is measured about the mean.

Another set of measures used in univariate analysis, complementing the study of the central tendency, involves statistical dispersion.

The mean difference is a measure of statistical dispersion equal to the average absolute difference of two independent values drawn from a probability distribution.

In practice we often prefer using different measures of central tendency and statistical dispersion to obtain a more comprehensive analysis of the relationship between variables .

In statistics, overdispersion is the presence of greater variability (statistical dispersion) in a data set than would be expected based on a given simple statistical model.

In statistics, the correlation ratio is a measure of the relationship between the statistical dispersion within individual categories and the dispersion across the whole population or sample.

The mode, median, and arithmetic mean are allowed to measure central tendency of interval variables, while measures of statistical dispersion include range and standard deviation.

In statistics, a robust measure of scale is a robust statistic that quantifies the statistical dispersion in a set of numerical data.

Those authors may judge whether data has a strong or a weak central tendency based on the statistical dispersion, as measured by the standard deviation or something similar.

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.

The variation ratio is a simple measure of statistical dispersion in nominal distributions; it is the simplest measure of qualitative variation.

The measures of statistical dispersion derived from absolute deviation characterize various measures of central tendency as minimizing dispersion:

In networking, in particular IP networks such as the Internet, jitter can refer to the variation (statistical dispersion) in the delay of the packets.

The Gini coefficient is a measure of statistical dispersion most prominently used as a measure of inequality of income distribution or inequality of wealth distribution.

In statistics, the analogous process is usually dividing a difference (a distance) by a scale factor (a measure of statistical dispersion), which yields a dimensionless number, which is called normalization.