Sensitivity Analysis Explainer
Sensitivity analysis tests how a model’s output changes when selected inputs change. It can show how profit responds to price or volume, how valuation responds to growth and discount rates, or how liquidity responds to collection delays. The method helps decision-makers identify which assumptions deserve the most attention.
A one-variable test changes one input while holding others constant. A two-variable table shows interactions between two drivers, and a stress test may move several inputs to adverse levels. Break-even analysis can identify the precise price, volume, or cost at which the decision changes. Ranges should reflect plausible uncertainty rather than arbitrary percentages.
Results are often presented in tables, charts, or ranked driver lists. Large output changes from small input changes can reveal model instability or a decision with little margin for error. Management can use that insight to gather better data, redesign a contract, add contingency, or set a monitoring threshold.
Sensitivity analysis is not a forecast of how variables will move together. Holding everything else constant may be unrealistic, and complex relationships can become nonlinear under stress. Use scenario analysis when a coherent story requires multiple linked changes. Document the baseline, tested ranges, dependencies, and response plan so the exercise supports action rather than decoration.
Test price, volume, cost, timing, rates, or other assumptions that drive the decision.
Use evidence-based ranges and keep the baseline visible.
Break-even points show where a conclusion changes.
Unexpectedly large output changes from small input changes can signal a fragile model or decision.
Sensitivity analysis often isolates variables, while scenarios link assumptions through a coherent narrative.
Use both when interactions and independent uncertainty are important.
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