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Most models and decisions rest on a handful of assumptions that matter enormously and many that barely matter at all—but people almost never know which is which until they test. Sensitivity analysis is the discipline of systematically varying inputs to discover where your model is fragile, revealing that confident-sounding conclusions often hinge on a single uncertain parameter. The deepest value isn't the numbers it produces but the intellectual humility it enforces: knowing what you don't know, and knowing which unknowns actually matter.
