Understanding the complex structures of social networks is essential to studies of social behaviour. Social structure has wide ecological and evolutionary implications for important ecological processes such as mate choice, social learning, cooperation, foraging, and disease transmission. It is therefore crucial that scientists use the appropriate tools and methods to study the properties of animal social networks. Despite the wide applicability and high profile of social network research in ecology, there is no established quantitative methodology to guide researchers in efficient and unbiased sampling of social networks. Ecologists attempt to capture network properties by recording observations of a select sample of animals and their social interactions. Their aim is to understand properties of the real network by constructing a sample network whose structure represents that of the real network. Then the sample network is analyzed using network theory. The assumption is that the sample network is structurally equivalent to the real network. For the scientific investigation to be valid and useful, this assumption must be met. This presents a problem: how can we be confident that the sampled animal network reliably represents the real-world network? If the sample network does not reliably represent the real network, then any conclusions inferred about the social behaviour occurring on the real network might be wrong.