  We present a test of different error estimators for 2-point clustering
statistics, appropriate for present and future large galaxy redshift surveys.
Using an ensemble of very large dark matter LambdaCDM N-body simulations, we
compare internal error estimators (jackknife and bootstrap) to external ones
(Monte-Carlo realizations). For 3-dimensional clustering statistics, we find
that none of the internal error methods investigated are able to reproduce
neither accurately nor robustly the errors of external estimators on 1 to 25
Mpc/h scales. The standard bootstrap overestimates the variance of xi(s) by
~40% on all scales probed, but recovers, in a robust fashion, the principal
eigenvectors of the underlying covariance matrix. The jackknife returns the
correct variance on large scales, but significantly overestimates it on smaller
scales. This scale dependence in the jackknife affects the recovered
eigenvectors, which tend to disagree on small scales with the external
estimates. Our results have important implications for the use of galaxy
clustering in placing constraints on cosmological parameters. For example, in a
2-parameter fit to the projected correlation function, we find that the
standard bootstrap systematically overestimates the 95% confidence interval,
while the jackknife method remains biased, but to a lesser extent. The scatter
we find between realizations, for Gaussian statistics, implies that a 2-sigma
confidence interval, as inferred from an internal estimator, could correspond
in practice to anything from 1-sigma to 3-sigma. Finally, by an oversampling of
sub-volumes, it is possible to obtain bootstrap variances and confidence
intervals that agree with external error estimates, but it is not clear if this
prescription will work for a general case.
