Dispersions are estimates of the variability of measurements in a given distribution.

Range

The range defines the distribution's width from the minimum to the maximum and is defined as follows:

Range  = x_max - x_min

Percentiles

The n-th percentile is the value which cuts n% of the distribution.

Variance and standard deviation

Variance and standard deviation are most commonly used to describe the variability of a given distribution.

Requirements: 

The individual measures must be at least interval scaled.

Calculation:

For the estimation of a population variance the variance of a sample distribution must be corrected as follows:

The same holds for the estimated standard deviation of a population

Standard error of the mean

This is a measure for the accuracy of the estimated population mean.

if is estimated from the sample:

Skewness

Skewness is a measure for the symmetry of a given distribution.
A negative skew means that the left tail of the distribution is elongated (AM > Median > Mode)
A positive skew means that the right tail of the distribution is elongated (AM < Median < Mode)

Examples:

Calculation: 

Skewness is the third standardized moment of the distribution and is computed as follows:

Sample skewness:

The estimation of the population skewness from a given sample is:

Kurtosis

Kurtosis is a measure of the “peakedness” of a given distribution. Higher kurtosis means more of the variance is due to infrequent extreme deviations, as opposed to frequent modestly-sized deviations.

Calculation:

Sample kurtosis:

The estimation of the population kurtosis is as follows: