Strengths and limitationsTypical strengths are:
 Requires very little resources and skills (but the choice of the aggregation level for the analysis is an important issue that does require skills)
 Quick (but can be dirty)
 Typical weaknesses are:
 Has a limited domain of applicability (e.g. nearlinearity assumption)
 The basic error propagation equations cannot cope well with distributions with other shapes than normal (but the method can be extended to account for other distributions).
 Leads to a tendency to assume that all distributions are normal, even in cases where knowledge of the shape is absent and hence a uniform distribution would be reflecting better the state of knowledge.
 Can not easily be applied in complex calculations
