  In many statistical problems, several estimators are usually available for
interval estimation of a parameter of interest, and hence, the selection of an
appropriate estimator is important. The criterion for a good estimator is to
have a high coverage probability close to the nominal level and a shorter
interval length. However, these two concepts are in opposition to each other:
high and low coverages are associated with longer and shorter interval lengths
respectively. Some methods, such as bootstrap calibration, modify the nominal
level to improve the coverage and thereby allow the selection of intervals
based on interval lengths only. Nonetheless, these methods are computationally
expensive. In this paper, we propose an index which offers an easy to compute
approach of comparing confidence interval estimators based on a compromise
between the coverage probability and the confidence interval length. We
illustrate that the confidence interval index has range of values within the
neighborhood of the range of the coverage probability, [0,1]. In addition, a
good confidence interval estimator has an index value approaching 1; and a bad
confidence interval has an index value approaching 0. A simulation study was
conducted to assess the finite sample performance of the index. The proposed
index is illustrated with a practical example from the literature
