Traps for monitoring of flying insect pests constitute a critical part of integrated pest management strategies. However, interpretation of trap captures is hampered by 1) factors associated with the performance of traps (i.e., lure, trap design, placement); 2) an often poorly defined relationship between trap captures and population density; and 3) interpretation approaches being highly specific to a certain insect species, trapping method, or trapping environment. The main purpose of this study was to identify a trap capture interpretation approach with little sensitivity to characteristics specific to a given data set, which would allow easier comparison of trapping data sets and make it easier to standardize sampling plans across insect pests and trapping environments. Based on fits of trapping data sets to standard distributions (normal, Poisson, and negative binomial), evaluations of the index of aggregation, k, and linear regression coefficients from Taylor’s power law, we concluded that these characteristics varied considerably among data sets, which means that enumerative sampling plans may not be appropriate. Across 13 trapping data sets of six insect species, we showed a consistent nonlinear relationship between average trap captures and number of traps with zero captures and that the k can be stabilized by converting trapping data into binomial data. A trap interpretation approach based on number of zero captures is both easy to use, was found to be species-independent, and means that it may be possible to establish meaningful and reliable action thresholds based on trap captures of flying insects. Although developed using trapping data from food facilities, this approach may have application to trapping data from other environments as well.