While measures of central tendency are used to estimate "normal" values of a dataset, measures of dispersion are important for describing the spread of the data, or its variation around a central value. Two distinct samples may have the same mean or median, but completely different levels of variability, or vice versa. A proper description of a set of data should include both of these characteristics. There are various methods that can be used to measure the dispersion of a dataset, each with its own set of advantages and disadvantages.
Locate Dataset and Variable 

Find Maximum Value  
View Maximum Values 

Find Minimum Values and Subtract from Maximum Values 

View Range 

Standard Deviations Away From Mean 
Abnormality 
Probability of Occurance 
beyond 3 sd 
extremely subnormal 
0.15% 
3 to 2 sd 
greatly subnormal 
2.35% 
2 to 1 sd 
subnormal 
13.5% 
1 to +1 sd 
normal 
68.0% 
+1 to +2 sd 
above normal 
13.5% 
+2 to +3 sd 
greatly above normal 
2.35% 
beyond +3 sd 
extremely above normal 
0.15% 
Locate Dataset and Variable 

Select Temporal and Spatial Domains 

Calculate Standard Deviation Values 

View Standard Deviation Values 
Equatorial Africa exhibits low standard deviation values of monthly cloud cover compared to regions to its north and south. High standard deviation values correspond to areas with large interannual cloud cover variability. Note that the root mean square anomaly can be substituted for the standard devation if the sample size is sufficiently large. (Devore, Jay L. Probability and Statistics for Engineering and the Sciences. pp. 3839, 259.) 
Locate Dataset and Variable 

Select Temporal and Spatial Domains 

Calculate Root Mean Square Anomaly 

View Root Mean Square Values 

Locate Dataset and Variable 

Select Temporal and Spatial Domains 

Compute Monthly Climatologies  
Calculate Interquartile Range 

View Interquartile Range 

Locate Dataset and Variable 
*NOTE: This example uses the same dataset and variable as the previous example.

Select Temporal and Spatial Domains 

Compute Monthly Climatologies  
Calculate Median Absolute Deviation 

View Median Absolute Deviation 
The higher the median absolute deviation, the more variability in the data. Similar to the IQR example, the Amazon Basin exhibits high intraannual precipitation variability, while areas to the north and south exhibit lower precipitation variability. 
Locate Dataset and Variable 

Select Temporal and Spatial Domains 

Calculate Spatial Average  
Find Trimmed Variance 
Calculate the trimmed variance by squaring the value above.
