The measure of location or averages or central tendency is not sufficient to describe the characteristics of a distribution, because two or more distributions may have averages that are exactly alike, even though the distributions are dissimilar in other aspects. On the other hand, a measure of central tendency represents the typical value of the data set. To give a sensible description of data, a numerical quantity called the measure of dispersion/ variability or scatter that describes the spread of the values in a set of data has two types of measures of dispersion or variability:
- Absolute Measures
- Relative Measures
A measure of central tendency together with a measure of dispersion gives an adequate description of data as compared to the use of a measure of location only, because the averages or measures of central tendency only describe the balancing point of the data set, it does not provide any information about the degree to which the data tend to spread or scatter about the average value. So, the Measure of dispersion is an indication of the characteristic of the central tendency measure. The smaller the variability of a given set, the more the values of the measure of averages will be representative of the data set.
Absolute measures are defined in such a way that they have units such as meters, grams, etc. same as those of the original measurements. Absolute measures cannot be used to compare the variation/spread of two or more data sets.
Most Common absolute measures of variability are:
Relative Measures of Dispersion
The relative measures have no units as these are ratios, coefficients, or percentages. Relative measures are independent of units of measurement and are useful for comparing data of different natures.
- Coefficient of Variation
- Coefficient of Mean Deviation
- Coefficient of Quartile Deviation
- Coefficient of Standard Deviation
Different terms are used for the measure of dispersion or variability such as variability, spread,