What does it mean that schema quality criteria?

Define what makes a schema good — accuracy predictive power simplicity scope.

Example: You have two mental models for why your team misses deadlines. Model A: 'People are lazy.' Model B: 'The estimation process consistently underweights integration complexity.' Both explain the same data. But Model B is more accurate (it points to a specific, verifiable mechanism), more predictive (it tells you which tasks will slip before they do), more actionable (you can fix the estimation process), and more testable (you can check whether tasks with high integration complexity slip more often). Model A feels explanatory but produces no usable predictions and no path to improvement. You need criteria to tell the difference.

Try this: Pick one schema you actively rely on — a belief about how your industry works, a model of what motivates your team, a theory about your own productivity patterns. Score it on each of the six criteria from this lesson (accuracy, predictive power, scope, simplicity, fruitfulness, falsifiability) using a 1-5 scale. Where does it score highest? Where lowest? The lowest-scoring criterion tells you the most about the schema's vulnerability.