Sensitivity Analysis
Version 1.0.0 · Updated 2026-07-30
CORE DEFINITION
The study of the extent to which model outputs (e.g., project profit) are affected by changes in model inputs (e.g., raw material prices, sales volume). That is, "If X changes by 1%, how much will Y change?"
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Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be apportioned to different sources of uncertainty in its inputs. A related practice is uncertainty analysis, which focuses more on the quantification of uncertainty and its propagation; ideally, uncertainty and sensitivity analysis should be performed together. Sensitivity analysis recalculates results under alternative assumptions to determine the impact of specified variables. It can be used for various purposes, including: Testing the robustness of the results of a model or system in the presence of uncertainty. Increasing understanding of the relationships between input and output variables in a system or model. Reducing uncertainty by identifying model inputs that cause significant uncertainty in the output. To improve robustness (possibly through further research), these inputs should become the focus of attention. Search...
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By changing model inputs or assumptions, observe the magnitude of change in outputs to identify key uncertain parameters. It quantifies the degree to which conclusions depend on assumptions.
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Source support: Explicit
- zh.wikipedia.orghttps://zh.wikipedia.org/wiki/%E6%95%8F%E6%84%9F%E5%BA%A6%E5%88%86%E6%9E%90verified
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