A concise exploration of the idea that underpins modern data thinking: models are approximations, not perfect representations. We trace the line from Korzybski and Box to Cox, Gelman, and beyond, unpacking the map-versus-territory metaphor and why usefulness matters more than perfection. With real-world examples like weather forecasts, we’ll discuss how to evaluate a model’s purpose, assumptions, and blind spots—and leave you with practical questions to ask before trusting its conclusions.

Note:  This podcast was AI-generated, and sometimes AI can make mistakes.  Please double-check any critical information.

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