مقایسهٔ روش‌ها

	تحلیل حساسیت با تحلیل درخت رویداد	تحلیل حساسیت همراه با تحلیل قابلیت اطمینان
حوزه	طراحی آزمایش	طراحی آزمایش
خانواده	Process / pipeline	Process / pipeline
سال پیدایش	Combination formalized in risk and reliability engineering from the 1990s onward	1969 (importance measures); 2000s (global SA integration)
پدیدآور	Sensitivity analysis: Saltelli et al. (1990s–2000s); Event tree analysis: Watson (1961, WASH-1400 formalization 1975)	Birnbaum (importance measures, 1969); Saltelli et al. (global SA formalization, 2000s)
نوع	Hybrid quantitative risk analysis method	Quantitative integrated engineering method
منبع بنیادین	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975	Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., & Tarantola, S. (2008). Global Sensitivity Analysis: The Primer. Wiley. ISBN: 978-0470059975
نام‌های دیگر	SA-ETA, ETA sensitivity analysis, event tree sensitivity analysis, probabilistic sensitivity analysis with ETA	SA-RA, reliability sensitivity analysis, importance measures in reliability, reliability-based sensitivity analysis
مرتبط	6	5
خلاصه	Sensitivity analysis with event tree analysis (SA-ETA) is a quantitative risk assessment approach that systematically varies the input probabilities of an event tree model to determine which branch probabilities or initiating event frequencies most strongly influence the calculated probability of undesired outcomes. It extends classical event tree analysis by ranking the uncertainty contributions of individual inputs, thereby guiding risk-reduction efforts toward the parameters that matter most.	Sensitivity analysis integrated with reliability analysis is a quantitative engineering method that determines how uncertainty or variation in each system input — such as component failure rates, material properties, or load distributions — propagates into overall system reliability. By computing importance measures for every uncertain parameter, analysts can rank components and assumptions by their influence on system dependability, focusing improvement efforts where they matter most.