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I will give a basic overview of statistical principles, both in terms of classical statistics and more modern machine learning perspectives. I will then discuss how sheaves can enter into statistical thinking both from a likelihood model based perspective as well direct models of data such as manifold learning. I will close with some concrete examples from Markov chain theory, modeling shapes and surfaces, as well as inference in dynamical systems.