In descriptive statistics, summary statistics are used to summarize a set of observations, in order to communicate the largest amount as simply as possible. Statisticians commonly try to describe the observations in

1. a measure of location, or central tendency, such as the arithmetic mean, median, mode, or interquartile mean
2. a measure of statistical dispersion like the standard deviation, variance, range, or interquartile range, or absolute deviation.
3. a measure of the shape of the distribution like skewness or kurtosis

The Gini coefficient was originally developed to measure income inequality, but can be used for other purposes as well.

Example

The following example using R is the standard summary statistics of a randomly sampled normal distribution, with a mean of 0, standard deviation of 1, and a population of 50:

> x <- rnorm(n=50, mean=0, sd=1)
> summary(x)
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-1.72700 -0.49650 -0.05157  0.07981  0.67640  2.46700

See also

• descriptive statistics
• statistical theory
• sufficient statistic