Big Data epistemology replaces population sampling with continuous extraction from whole populations

Evidence

If Big Data reasoning relies on the predictive power that comes from repetitive processing of unstructured data, this data can be generated, directly or indirectly, from whole populations. It no longer relies on the carefully constructed samples of the general population on which classical statistical knowledge relied. This new model of knowledge requires collecting data continuously, so that N might as well be all. — Nick Couldry & Ulises A. Mejias, The Costs of Connection, 398

Categorization

• XREF: The shift from samples to whole populations is a key point of divergence between data-colonial and attention-based critiques of platform power. — connects to: Big Data logic rejects theory-driven hypothesis testing in favor of raw pattern extraction — Nick Couldry & Ulises A. Mejias, The Costs of Connection: How Data Is…, 394; Biopolitical population statistics cannot reach the psyche that Big Data exploits — Byung-Chul Han, Psychopolitics: Neoliberalism and New…, 58
• SEED: Potential essay: how the abandonment of statistical sampling doctrine quietly removed the methodological justification for population representation.