Nilotpal Sanyal

Statistics and data science aim to extract knowledge from incomplete and noisy observations through probabilistic modeling and inference. Advaita Vedanta addresses a related epistemological problem---how human cognition reveals an empirical world that reflects reality only partially because of \emph{avidyā} (ignorance). This paper develops a conceptual dialogue between these domains by arguing that statistical models, like the Advaita Vedantic account of \emph{māyā} and \emph{adhyāsa}, provide structured but mediated access to what underlies appearances. The comparison is developed through several distinct themes---model-dependent knowledge and mediated experience, hierarchical models and layered reality, signal extraction and conceptual filtering, overfitting and abstraction from incidental appearances, usefulness without literal truth, workflow of inquiry, and provisional conceptual scaffolding. The aim is not to claim historical influence or doctrinal identity, but to illuminate shared epistemic concerns regarding representation, approximation, latent structure, uncertainty, and the limits of knowledge. By placing these traditions in dialogue, this work not only illuminates shared structural features between Advaita and statistical science but also suggests how cross-traditional reflection on model-dependent knowledge may open avenues for novel insights.