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Sensitivity Analysis

Why It Matters

Not all variables are equally important. Sensitivity analysis identifies where precision matters and where rough estimates suffice. It focuses effort.

The Key Move

For each input, ask: if this were 10% higher or lower, how much would the outcome change? Inputs with high sensitivity deserve more attention (better estimates, tighter control). Inputs with low sensitivity can be approximated.

Classic Application

Financial modeling. Does the valuation depend more on growth rate or discount rate? Sensitivity analysis reveals which assumptions to scrutinize.

Surprising Application

Cooking. Some ingredients are high-sensitivity (salt, acid) - small changes matter a lot. Others are low-sensitivity (exact amount of vegetables) - you can be approximate. Skilled cooks intuitively do sensitivity analysis.

Failure Modes

  • Treating all inputs as equally important
  • Ignoring interactions (sensitivity of A may depend on the level of B)
  • Confusing sensitivity with importance (a highly sensitive input might have low range in practice)

Go Deeper

Any optimization or decision analysis text. Monte Carlo simulation for complex cases.