Sensitivity Analysis
Definition
A sensitivity analysis shows how strongly changed assumptions or inputs influence a result.
The investigation of how strongly results react to changes in inputs, parameters, or assumptions.
Simply explained
You turn individual knobs and observe which ones cause the result to change particularly strongly.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A business case varies usage rate, hourly rate, and implementation costs.
Limits and pitfalls
- Changing only one factor at a time, ignoring interactions, or not justifying plausible ranges.
Origin & history
Sensitivity analysis originates from modeling, decision theory, and uncertainty analysis.
Use in mid-sized companies
- Possible example: A business case varies usage rate, hourly rate, and implementation costs.
Frequently asked questions
What does Sensitivity Analysis mean?
The investigation of how strongly results react to changes in inputs, parameters, or assumptions. What mistakes should be avoided? Changing only one factor at a time, ignoring interactions, or not justifying plausible ranges.
Sources
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: