  Confidence intervals are an established means of portraying uncertainty about
an inferred parameter and can be generated through the use of confidence
distributions. For a confidence distribution to be ideal, it must maintain
frequentist coverage of the true parameter. This can be represented for a
precise distribution by adherence to a cumulative unit uniform distribution,
referred to here as a Singh plot. This manuscript extends this to imprecise
confidence structures with bounds around the uniform distribution, and
describes how deviations convey information regarding the characteristics of
confidence structures designed for inference and prediction. This quick visual
representation, in a manner similar to ROC curves, aids the development of
robust structures and methods that make use of confidence. A demonstration of
the utility of Singh plots is provided with an assessment of the coverage of
the ProUCL Chebyshev upper confidence limit estimator for the mean of an
unknown distribution.
