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

Sampling bias in systems with structural heterogeneity and limited internal diffusion