  Skewness measures can be used to measure the level of asymmetry of a
distribution. Given the prevalence of statistical methods that assume
underlying symmetry, and also the desire for symmetry in order to make
meaningful judgements for common summary measures (e.g. the sample mean),
reliably quantifying asymmetry is an important problem. There are several
measures, among them generalizations of Bowley's well known skewness
coefficient, that use sample quartiles and other quantile-based measures. The
main drawbacks of many measures is that they are either limited to quartiles
and do not take into account more extreme tail behavior, or that they require
one to choose other quantiles (i.e. choose a value for $p$ different from 0.25)
in place of the quartiles. Our objective is to (i) average the skewness
measures over all $p$ and (ii) provide interval estimators for the new measure
with good coverage properties. Our simulation results show that the interval
estimators perform very well for all distributions considered.
