  Complex systems research is becomingly increasingly data-driven, particularly
in the social and biological domains. Many of the systems from which sample
data are collected feature structural heterogeneity at the mesoscopic scale
(i.e. communities) and limited inter-community diffusion. Here we show that the
interplay between these two features can yield a significant bias in the global
characteristics inferred from the data. We present a general framework to
quantify this bias, and derive an explicit corrective factor for a wide class
of systems. Applying our analysis to a recent high-profile survey of conflict
mortality in Iraq suggests a significant overestimate of deaths.
