  Models of weak-scale supersymmetry offer viable dark matter (DM) candidates.
Their parameter spaces are however rather large and complex, such that pinning
down the actual parameter values from experimental data can depend strongly on
the employed statistical framework and scanning algorithm. In frequentist
parameter estimation, a central requirement for properly constructed confidence
intervals is that they cover true parameter values, preferably at exactly the
stated confidence level when experiments are repeated infinitely many times.
Since most widely-used scanning techniques are optimised for Bayesian
statistics, one needs to assess their abilities in providing correct confidence
intervals in terms of the statistical coverage. Here we investigate this for
the Constrained Minimal Supersymmetric Standard Model (CMSSM) when only
constrained by data from direct searches for dark matter. We construct
confidence intervals from one-dimensional profile likelihoods and study the
coverage by generating several pseudo-experiments for a few benchmark sets of
pseudo-true parameters. We use nested sampling to scan the parameter space and
evaluate the coverage for the benchmarks when either flat or logarithmic priors
are imposed on gaugino and scalar mass parameters. The sampling algorithm has
been used in the configuration usually adopted for exploration of the Bayesian
posterior. We observe both under- and over-coverage, which in some cases vary
quite dramatically when benchmarks or priors are modified. We show how most of
the variation can be explained as the impact of explicit priors as well as
sampling effects, where the latter are indirectly imposed by physicality
conditions. For comparison, we also evaluate the coverage for Bayesian credible
intervals, and observe significant under-coverage in those cases.
