  For galaxy clustering to provide robust constraints on cosmological
parameters and galaxy formation models, it is essential to make reliable
estimates of the errors on clustering measurements. We present a new technique,
based on a spatial Jackknife (JK) resampling, which provides an objective way
to estimate errors on clustering statistics. Our approach allows us to set the
appropriate size for the Jackknife subsamples. The method also provides a means
to assess the impact of individual regions on the measured clustering, and
thereby to establish whether or not a given galaxy catalogue is dominated by
one or several large structures, preventing it to be considered as a "fair
sample". We apply this methodology to the two- and three-point correlation
functions measured from a volume limited sample of M* galaxies drawn from data
release seven of the Sloan Digital Sky Survey (SDSS). The frequency of
jackknife subsample outliers in the data is shown to be consistent with that
seen in large N-body simulations of clustering in the cosmological constant
plus cold dark matter cosmology. We also present a comparison of the
three-point correlation function in SDSS and 2dFGRS using this approach and
find consistent measurements between the two samples.
