Strengths and limitations

Typical 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. near-linearity 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