20 published lessons with this tag.
An untested schema is a hypothesis not knowledge.
If no possible observation could prove your schema wrong it is not a useful model.
Create specific tests that would show you if your mental model is accurate.
If your schema is correct it should make accurate predictions about what will happen next.
When your prediction is wrong you have learned something about where your schema is off.
Unusual or extreme situations reveal where your schema breaks down.
Explaining your schema to someone else and hearing their objections is a form of validation.
The most reliable way to test a schema is to act on it and observe the results.
Test the smallest piece of your schema first before relying on the whole structure.
Looking for evidence that supports your schema is not the same as rigorously testing it.
Deliberately try to break your own mental model before relying on it.
Testing takes time and energy — validate the schemas that matter most first.
When direct testing is impossible look for indirect evidence and converging indicators.
Having trusted people review your mental models catches errors you miss.
Recording what you tested and what happened creates a validation history.
Even a well-tested schema may fail in new contexts or at different scales. Validation tells you where a schema works, not that it works everywhere. The boundaries of your tested conditions are the boundaries of your warranted confidence.
Confidence based on tested schemas is categorically different from confidence based on untested assumptions.
Finding out your schema is wrong teaches you more than confirming it is right.
Schemas need ongoing testing because the world they model keeps changing.
Testing your beliefs against reality is the core practice of intellectual integrity. Epistemic honesty is not a personality trait — it is a discipline you build by systematically subjecting your schemas to evidence, welcoming disconfirmation, and refusing to protect comfortable models from uncomfortable data.