Scientific understanding of multitactic weed management systems (MTS) is complicated by (1) the large number of potential combinations among tactics, (2) potentially noisy and complex system behavior because of individually more moderate mortality events, and (3) possible transient system behavior of unknown duration. Therefore, decomposing the relative performance of MTS components is much more difficult than it is for single-tactic strategies (STS). Attempting to accommodate the increased complexity of system behavior while maintaining the generality of results requires analytical methods capable of accomplishing these tasks. We provide two examples of statistical procedures that may help gain understanding of MTS systems using previously published weed demographic time-series data. First, we demonstrate the use of mixed-effects models capable of representing and removing factors contributing uncontrolled variation to system behavior. Model selection criteria are used to highlight the importance of the increased flexibility the mixed-model framework provides. Second, by explicitly modeling the probabilistic process presumed to be generating the data, we demonstrate how different components of the MTS can be compared and how the methodology can facilitate integration of such information into a decision-making application.