  We discuss irreducible statistical limitations of future ton-scale dark
matter direct detection experiments. We focus in particular on the coverage of
confidence intervals, which quantifies the reliability of the statistical
method used to reconstruct the dark matter parameters, and the bias of the
reconstructed parameters. We study 36 benchmark dark matter models within the
reach of upcoming ton-scale experiments. We find that approximate confidence
intervals from a profile-likelihood analysis exactly cover or over-cover the
true values of the WIMP parameters, and are hence conservative. We evaluate the
probability that unavoidable statistical fluctuations in the data might lead to
a biased reconstruction of the dark matter parameters, or large uncertainties
on the reconstructed parameter values. We show that this probability can be
surprisingly large, even for benchmark models leading to a large event rate of
order a hundred counts. We find that combining data sets from two different
targets leads to improved coverage properties, as well as a substantial
reduction of statistical bias and uncertainty on the dark matter parameters.
