🔬Data Science: Sensitivity Analysis (Definitions)

"The practice of changing a variable in a financial model or forecast to determine how a change in that variable affects the overall outcome. For example, to consider the way in which a change in price might affect the gross profit in a product forecast, one might vary the price in small increments and recompute the figures to see how gross profit changes." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"Sensitivity analysis is a methodology for assessing whether an empirical effect is a valid causal effect. The basic idea is to simulate the change in the empirical effect that would result under plausible assumptions about the possible impact of the most likely sources of bias." (Herbert I Weisberg, "Bias and Causation: Models and Judgment for Valid Comparisons", 2010)

"Use of quantitative and qualitative information to study changes in results that would occur with changes in various assumptions. Also see best-case and worst-case scenario." (Leslie G Eldenburg & Susan K Wolcott, "Cost Management 2nd Ed", 2011)

"Study of the impact that changes in one or more parts of a model have on other parts or the outcome." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed, 2011)

"A quantitative risk analysis and modeling technique used to help determine which risks have the most potential impact on the project. It examines the extent to which the uncertainty of each project element affects the objective being examined when all other uncertain elements are held at their baseline values. The typical display of results is in the form of a tornado diagram." (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies®", 2011)

"A form of simulation modeling that focuses specifically on identifying the upper and lower bounds of model outputs given a series of inputs with specific variance." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"An analysis used in mathematical modelling, where the sensitivity of model results to variations in a particular variable is studied." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"An analysis technique to determine which individual project risks or other sources of uncertainty have the most potential impact on project outcomes, by correlating variations in project outcomes with variations in elements of a quantitative risk analysis model." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide )", 2017)

"An analysis that involves calculating a decision model multiple times with different inputs so a modeler can analyze the alternative results." (Ciara Heavin & Daniel J Power, "Decision Support, Analytics, and Business Intelligence 3rd Ed.", 2017)

"A technique used to determine how different values of an independent variable will impact a particular dependent variable under a given set of assumptions. It allows an analyst to determine whether a statistical finding will remain consistent under a variety of conditions. |" (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)