There are multiple quantitative methods for calculating and graphically illustrating statistical spreads. Among the most useful are calculating and graphic standard deviations.
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It is one of several measures of the spread of data. It is easier to calculate than the standard deviation, which has important statistical properties.
The standard deviation is a measure of how spread out the numbers are. Three points is needed to calculate a statistically valid meaningful standard deviation.
It is a measure of the spread of a set of observations. It is easy to calculate and is not distorted by extreme values (or mistakes). On the other hand it does not use all of the information contained in the data set.
There are several different measures of average spread: the standard deviation is the most common but average absolute deviation is another possibility. You could also have total ranges or inter-quartile ranges from sets of observations, each one being a measure of spread. Their averages would also be average spreads. You would have to calculate the average spread according to whichever definition you wanted and then round up or down to 2 decimal places (or the nearest hundredths).
It gives an indication of the spread in the values: are they all very close to the mean value or scattered across a wide range of values? That is important in determining how accurate your point estimate of the mean is.