PMI-RMP · Question #148
Sensitivity analysis is a technique for systematically changing parameters in a model to determine the effects of such changes and is useful for computer modelers for a range of purposes. Which of…
The correct answer is A. Increased understanding or quantification of the system C. Model development D. Decision making or the development of recommendations for decision makers. Sensitivity analysis serves multiple purposes including increasing system understanding, supporting model development, and enabling decision making, but estimating the average outcome is not one of its purposes.
Question
Sensitivity analysis is a technique for systematically changing parameters in a model to determine the effects of such changes and is useful for computer modelers for a range of purposes. Which of the following purposes does the sensitivity analysis include? Each correct answer represents a complete solution. Choose all that apply.
Options
- AIncreased understanding or quantification of the system
- BEstimating the average outcome
- CModel development
- DDecision making or the development of recommendations for decision makers
How the community answered
(19 responses)- A89% (17)
- B11% (2)
Why each option
Sensitivity analysis serves multiple purposes including increasing system understanding, supporting model development, and enabling decision making, but estimating the average outcome is not one of its purposes.
Sensitivity analysis increases understanding or quantification of a system by revealing which input parameters have the greatest influence on outputs, helping analysts identify the most critical variables driving risk.
Estimating the average outcome is the purpose of expected value calculations or Monte Carlo simulation, not sensitivity analysis - sensitivity analysis focuses on how output changes relative to input variation, not on computing a central tendency.
Sensitivity analysis supports model development by helping modelers determine which parameters require precise estimation and which can be simplified without materially affecting model accuracy.
Sensitivity analysis directly supports decision making by demonstrating how robust conclusions are to changes in key assumptions, allowing decision makers to understand the range of possible outcomes before committing to a course of action.
Concept tested: Purposes of sensitivity analysis in quantitative risk modeling
Source: https://www.pmi.org/pmbok-guide-standards/foundational/pmbok
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