Technical Abstract: Statistically robust sampling strategies form an integral component of grain storage and handling activities throughout the world. Developing sampling strategies to target biological pests such as insects in stored grain is inherently difficult due to species biology and behavioral characteristics. The design of sampling programs should be based on an underlying statistical distribution which is sufficiently flexible to capture variations in the spatial distribution of the target species. In this paper we compare the accuracy of four sampling approaches, the negative binomial model, the Poisson model, the double logarithmic model and the compound model, to detect insects over a range of insect densities. Although the double log and negative binomial models performed well under specific conditions, we show that, of the four models examined, the compound model performed the best over a broad range of distributions. Unlike the double logarithmic approach and negative binomial approach, the compound model was shown to be robust when insects were restricted to few samples but density was high. The Poisson approach consistently underperformed, failing to detect insects at the 0.95 detection threshold in comparison to the other three models examined. This paper illustrates that the compound model is robust over a broad data range and leads to substantial improvement of detection probabilities within highly variable systems such as grain storage.