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Aurelius · Work & Leadership
Knowledge + Guidance

I can fit a model — why don't I understand its assumptions?

You can get the numbers out. You press the button, the fit looks reasonable, and you move on. This is not understanding — it is operation. A man can drive a chariot without knowing why the wheel turns. You are driving. The danger is not that you lack skill. The danger is that the model will fail exactly when you need it most — on data it was never built for — and you won't see it coming, because you never asked what it requires to be true in the first place. This is not fixed by reading more. It is fixed by taking your own work and trying, on purpose, to make it fall apart.

◆ How this problem reads on the two dials
GuidanceKnowledge
Coaching
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1:1 with AureliusWith others (a Pod)
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A little with peers
There is real content to learn — what assumptions mean and how they fail — but the gap closes only through the practice of deliberately stress-testing your own model, not more reading.
How the two dials adapt to you →
What’s really going on

You fit the model because the tool let you. You don't understand it because you've never tried to break it. This isn't a knowledge gap you lack time for — it's a test you've been avoiding. Choose one assumption your model makes. Go prove it wrong on purpose. That is how understanding gets built.

🔒 What you’ll build togetherUnlock by starting
A moveTake the last model you built. Write down, in plain words, the three things it assumes are true about your data.
A movePick one of those assumptions. Change the input until it's clearly false. Watch what the output does.
A moveFind one real case in your own data where that assumption doesn't hold. Report, in a sentence, what happened to the result.
A moveAsk someone who knows the model better than you: 'what would make this lie to us?' Write the answer down before you forget it.
A moveBefore you hand over any result, state the one condition under which that result is wrong.

What changes unlock by starting

  • You can name the specific conditions under which your model fails — not just admit vaguely that it 'might.'
  • You stop trusting output by default and start checking it the way you'd check a tool with known limits.
  • You can explain your model's assumptions to a non-expert in plain language.
  • You catch a bad result before it becomes a bad decision, instead of after.

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