EPISODE · Jul 24, 2025 · 5 MIN
All Models Are Wrong, But Some Are Useful: A Practical Dive into Modeling
from Intellectually Curious · host Mike Breault
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.Sponsored by Embersilk LLC
What this episode covers
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. No...
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All Models Are Wrong, But Some Are Useful: A Practical Dive into Modeling
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