Sensitivity Analysis

Sensitivity Analysis quantifies the impact of variable changes on a specific outcome within a model. Employed across various disciplines, it aids in risk assessment, model validation, and decision-making, offering metrics to represent sensitivity.


Quantitative technique to assess how different values of an independent variable impact a particular dependent variable under a given set of assumptions.

Types of Variables

  • Input Variables: Factors being manipulated in the analysis.
  • Output Variables: Resultant variables that change based on input variations.

Types of Sensitivity Analysis

  • Local Sensitivity Analysis: Examines change over a small range of parameter values.
  • Global Sensitivity Analysis: Covers a wide range of parameter values.
  • Deterministic: Uses specific set values for inputs.
  • Probabilistic: Incorporates randomness in inputs and/or outputs.


  • One-at-a-Time (OAT): Changes one variable while keeping others constant.
  • Monte Carlo Simulation: Uses random sampling to obtain numerical results.
  • Factorial Analysis: Investigates the effects of multiple variables at once.


  • Risk Assessment: Used to estimate uncertainties in outcomes.
  • Model Validation: Helps in refining models by comparing results to real-world data.
  • Decision Support: Assists in choosing between different strategies or scenarios.

Metrics and Indicators

  • Elasticity: Measure of sensitivity, often expressed as a percentage change.
  • Tornado Diagrams: Graphical representation ranking variables by their impact.
  • Sobol Indices: Quantify the contribution of each input to the output variance.


  • Computational Complexity: Especially relevant for high-dimensional models.
  • Assumptions: Results as good as the assumptions they are based on.

Interdisciplinary Usage

  • Finance: Option pricing, portfolio optimization.
  • Engineering: System reliability, material selection.
  • Medicine: Epidemiological models, treatment effectiveness.

Ethical Considerations

  • Transparency: Clear methodology essential for validity.
  • Misuse: Risk of cherry-picking data or manipulating variables for desired outcomes.