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	Analiza Wrażliwości Stochastycznej	Analiza Scenariuszowa Stochastyczna
Dziedzina	Symulacja	Symulacja
Rodzina	Process / pipeline	Process / pipeline
Rok powstania	1990s–2000s	1955–1980s
Twórca	Saltelli, A. et al.; Claxton, K. et al. (health economics stream)	Dantzig, G. B.; Birge, J. R.; and others in stochastic programming tradition
Typ	Probabilistic uncertainty quantification technique	Probabilistic scenario enumeration and evaluation
Źródło pierwotne	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 9780470059975	Birge, J. R., Louveaux, F. (2011). Introduction to Stochastic Programming (2nd ed.). Springer. ISBN: 9781461402374
Inne nazwy	PSA, Probabilistic Sensitivity Analysis, Stochastic SA, Monte Carlo Sensitivity Analysis	Probabilistic Scenario Analysis, SSA, Stochastic What-If Analysis, Monte Carlo Scenario Analysis
Pokrewne	5	4
Podsumowanie	Stochastic Sensitivity Analysis (PSA) extends classical one-at-a-time sensitivity testing by representing uncertain model inputs as probability distributions and propagating them through the model via Monte Carlo sampling. The result is a full distribution of possible outputs, together with rankings of which inputs drive output variance the most — enabling robust, evidence-grounded conclusions under uncertainty.	Stochastic Scenario Analysis evaluates a system or decision across multiple explicitly defined scenarios, each assigned a probability of occurrence. Unlike deterministic scenario analysis, it propagates uncertainty through probability distributions and computes expected outcomes, variance, and risk metrics across the scenario space, giving decision-makers a structured view of what could happen and how likely each outcome is.