Template:M intro design how the laws of data science lie: Difference between revisions

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-In 1983 philosopher [[Nancy Cartwright]] wrote the seemingly scurrilous {{Br|How the Laws of Physics Lie}} — not quite the post-modernist tripe it sounds like, but rather a serious and literate work of analytical philosophy. Cartwright’s point was that scientific laws are formulated in conditions so rigid, isolated and controlled that, even though they might be perfectly valid within those conditions, they are practically useless “in the real world,” where those conditions have no hope of existing. So the principles of Newton’s mechanics, assuming as they do no inconveniently intervening forces like friction, gravity, inelasticity, might plat the trajectory of an object on a graph, but have no chance of plotting the trajectory of the proverbial [[Crisp-packet blowing across St Mark’s square|crisp packet blowing across St Mark’s Square]].
+In 1983 philosopher [[Nancy Cartwright]] wrote the seemingly scurrilous {{Br|How the Laws of Physics Lie}} — not quite the post-modernist tripe it sounds, but rather a serious and literate work of analytical philosophy. Cartwright’s point was that scientific laws are formulated in conditions so rigid, isolated and controlled that, even though they might be perfectly valid within those conditions, they are practically useless “in the real world,” where those conditions have no hope of existing. So the principles of Newton’s mechanics, assuming as they do no inconveniently intervening forces like friction, gravity, inelasticity, might plat the trajectory of an object on a graph, but have no chance of plotting the trajectory of the proverbial [[Crisp-packet blowing across St Mark’s square|crisp packet blowing across St Mark’s Square]]. You will spend a lot of time with a slide rule and an anemometer; when you look up the packet will be gone.
+The same observation animates [[Gerd Gigerenzer]]’s faith in heuristics over science: despite [[Richard Dawkins]]’ trite conviction, a fielder performs no differential equations on the way to catching a flying cricket ball.
+We have a sense a similar thing may be true of data science.