Toolcatalogus

1.	Sensitivity analysis

2.	Error propagation equation ("TIER 1")

3.	Monte Carlo analysis ("TIER 2")

5.	Expert elicitation for uncertainty quantification

6.	Scenario analysis

7.	PRIMA: A framework for perspective-based uncertainty management

8.	Checklist for model quality assistance

9.	A method for critical review of assumptions in model-based assessments

Typical pitfalls of Monte Carlo Analysis are:

Forgetting that Monte Carlo analysis takes the model structure and boundaries for granted
Ignoring correlations
Hyper precision: Often the PDFs on the inputs used have the status of educated guesses. The output produced by the software packages usually come out the computer with a high number of digits, which are certainly not significant. Also the shapes of the input distributions are usually not well known, therefore one should not attribute too much meaning to the precise shape of the distribution as it comes out of the calculation.
Glossy reports: Present day software packages for Monte Carlo Analysis can be used easily without requiring prior knowledge of Monte Carlo analysis or prior theoretical knowledge of probability distributions theory. The somewhat glossy results produced by the computer look very professional even if the experiment was poorly designed. We therefore recommend not using these packages without understanding the basics of probability distributions theory, correlations and Monte Carlo analysis. The handbooks that go with the software provide good primers on these issues. We particularly recommend the Crystal Ball handbook in this respect.
Note that several software packages for Monte Carlo Analysis (inter alia SimLab, and Crystal Ball) give false results if Windows is configured to use a comma as decimal separator rather than a dot.