We discuss how sampling design, units, the observation mechanism, and other basic statistical notions figure into modern network data analysis. These con- siderations pose several new challenges that cannot be adequately addressed by merely extending or generalizing classical methods. Such challenges stem from fundamental differences between the domains in which network data emerge and those for which classical tools were developed. By revisiting these basic statistical considerations, we suggest a framework in which to develop theory and methods for network analysis in a way that accounts for both conceptual and practical chal- lenges of network science. We then discuss how some well known model classes fit within this framework.