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Increasingly convinced that high-res metrics, when used as a proxy for some other thing, are almost always worse than fuzzy rules of thumb that deal with the thing directly. It's VERY difficult to get people to intuit that a weak measure can have extremely high apparent precision
Basically: it is not the end of the world if your tools are a bit wonky, messy, or hard to point, BUT they should **look** wonky/messy/hard to point. It should be obvious. "This is wonky" does not belong in the 3rd paragraph of the implementation document nobody reads
So we don't need analysts or data science. Any idiot with "high level" (often code for superficial) rules of thumb will do?
Yes, this is obviously exactly what I'm saying. Feel free to settle in to what looks like a very comfortable sense of impulsive superiority.
Newtonian vs. quantum mechanics comes to mind. For the measure to be meaningful, it must be at the same resolution level and emergence level that you are measuring. E.g. analysis of pixels on screen is meaningless for a movie review.
And there’s the unintended consequence that the evaluated will strive to game the metric, standardize themselves, and then defend the standard which clipped them because now it’s how they know themselves. This is why tests measure so much less than a conversation.
What's the connection between something being directly related to a thing and it being fuzzy?
Examples?
Any concrete examples?
The false authority of numbers
It's similar to the use of significant digits— if you can't actually measure to within a nanometer, you shouldn't tell people you're 197.0614070 centimeters tall.
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