Talk it through with Aurelius
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Aurelius · Work & Leadership
Knowledge + Guidance

I can fit a model, but I don't know when it breaks. Now what?

You can turn the crank. Data goes in, numbers come out, and the numbers look reasonable. That is not the same as knowing what the model assumes, or when those assumptions break. Most people in this spot never test the edges. They trust the output because it ran without an error. An error message is not the same as truth. Your model can run clean and still lie to you. You are not alone with this. Your team has the same gap, even if each of you hides it differently. Use that. Stop asking who understands it best and start asking where it breaks, together.

◆ How this problem reads on the two dials
GuidanceKnowledge
Coaching
More to learn
1:1 with AureliusWith others (a Pod)
Some one-to-one
Practise with peers
Half of this is a real knowledge gap about what the model assumes, and half is the avoided work of testing it, so teach the concepts first and then coach the practice of breaking the model.
How the two dials adapt to you →
What’s really going on

You do not fix this by reading more theory alone. You fix it by breaking your own model on purpose, with others watching, until you see where it bends. Choose one assumption. Test it against data you know violates it. Let your team find the crack before reality does.

🔒 What you’ll build togetherUnlock by starting
A movePick one assumption your model depends on. Write down what happens if it's false.
A moveFeed the model data you know violates its assumptions. Watch it fail on purpose.
A moveExplain the model's limits out loud to your team, without the math. If you can't, you don't own it yet.
A moveTrade models with a teammate. Find the break point in theirs before they find yours.
A moveKeep a running list titled 'Where this model lies.' Add to it every week.
PractiseBreak It Together · a Pod of 4 · 30 min

What changes unlock by starting

  • You can name three conditions that would make your model wrong.
  • Your team stops treating model output as settled fact.
  • You catch a bad assumption before it reaches a decision, not after.
  • You can explain the model's limits to someone outside the field, in plain words.

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