What does 'Quintiles' mean

A quintile is a statistical value of a data set that represents 20% of a given population, so the first quintile represents the lowest fifth of the data (1-20%); the second quintile represents the second fifth (21% - 40%) and so on.

Quintiles are often used to create cut-off points for a given population: a government sponsored socio-economic study may use quintiles to determine the maximum wealth a family could possess in order to belong to the lowest quintile of society. This cut-off point can then be used as a prerequisite for a family to receive a special government subsidy aimed to help society's less fortunate.


A quintile is a type of quantile, which is defined as equal-sized segments of a population. One of the most common metrics in statistical analysis, the median, is actually just the result of dividing a population into two quantiles. A population split into three equal parts is divided into tertiles, while one split into fourths is divided into quartiles. The larger the data set, the easier it is to divide into greater quantiles. Economists often use quintiles to analyze very large data sets, such as the population of the United States.

Common Uses of Quintiles

Politicians invoke quintiles to illustrate the need for policy changes. For example, a politician who champions economic justice can divide the population into quintiles to illustrate how the top 20% of income earners controls what, in his opinion, is an unfairly large share of the wealth. On the other end of the spectrum, a politician calling for an end to progressive taxation might use quintiles to make the argument that the top 20% shoulder too large a share of the tax burden.

In "The Bell Curve," a controversial 1994 book on intelligence quotient (IQ), the authors use quintiles throughout the text to illustrate their research showing that IQ is heavily correlated with positive outcomes in life.

Alternatives to Quintiles

For certain populations, the use of other methods to examine how the data is distributed make more sense than quintiles. For smaller data sets, the use of quartiles or tertiles helps prevent the data from being spread too thin. Comparing the mean, or average, of a data set to its median, or the cutoff point when the data is divided into two quantiles, reveals if the data is evenly distributed or if it is skewed toward the top or bottom. A mean that is significantly higher than the median indicates the data is top-heavy, while a lower mean suggests the opposite.