The estimation of test metrics such as defect level, test yield or yield loss is important in order to quantify the quality and cost of a test approach. In the analogue domain, previous works have considered the estimation of these metrics for the case of single faults, either catastrophic or parametric. The consideration of single parametric faults is sensible for a production test technique if the design is robust. However, in the case that production test limits are tight, test escapes resulting from multiple papametric deviations become important. In addition, aging mechanisms result in field failures that are often caused by multiple parametric deviations. In this paper, we present a statistical technique for estimating test metrics for the case of multiple analogue parametric deviations, requiring a Monte Carlo simulation of the circuit under test. This technique assumes Gaussian Probability Density Functions (PDFs) for the parameter and performance deviations but the technique can be adapted to other types of PDFs. We will illustrate the technique for the case of testing a fully differential operational amplifier, proving the validity in the case of this circuit of the Gaussian PDE.