Principlev1
Separate decision quality from outcome quality in
Separate decision quality from outcome quality in post-decision analysis, as conflating the two (resulting) causes your risk schema to update on noise rather than signal and converges on superstition rather than calibration.
Why This Is a Principle
Derives from Double-loop learning requires questioning the framework (double-loop learning questions framework), Learning occurs when outcomes differ from predictions, (learning from prediction error), and Raw experience, without reflection, does not produce (experience without reflection doesn't produce learning). Prescribes a specific evaluation practice to prevent schema corruption. Highly actionable—tells you exactly what to separate in analysis.