There are a large number of different definitions used for
sample quantiles in statistical computer packages. Often
within the same package one definition will be used to
compute a quantile explicitly, while other definitions may
be used when producing a boxplot, a probability plot, or a
QQ plot. We compare the most commonly implemented sample
quantile definitions by writing them in a common notation
and investigating their motivation and some of their
properties. We argue that there is a need to adopt a
standard definition for sample quantiles so that the same
answers are produced by different packages and within each
package. We conclude by recommending that the
median-unbiased estimator be used because it has most of
the desirable properties of a quantile estimator and can be
defined independently of the underlying distribution.
%0 Journal Article
%1 hyndman.fan:sample
%A Hyndman, Rob J.
%A Fan, Yanan
%D 1996
%J The American Statistician
%K imported statistic
%N 4
%P 361--365
%R 10.2307/2684934
%T Sample Quantiles in Statistical Packages
%U http://www.jstor.org/stable/2684934
%V 50
%X There are a large number of different definitions used for
sample quantiles in statistical computer packages. Often
within the same package one definition will be used to
compute a quantile explicitly, while other definitions may
be used when producing a boxplot, a probability plot, or a
QQ plot. We compare the most commonly implemented sample
quantile definitions by writing them in a common notation
and investigating their motivation and some of their
properties. We argue that there is a need to adopt a
standard definition for sample quantiles so that the same
answers are produced by different packages and within each
package. We conclude by recommending that the
median-unbiased estimator be used because it has most of
the desirable properties of a quantile estimator and can be
defined independently of the underlying distribution.