Technical Paper
Type X and Y Errors and Data & Model Conditioning for Systematic Uncertainty in Model Calibration, Validation, and Extrapolation1
2008-04-14
2008-01-1368
This paper introduces and develops the concept of “Type X” and “Type Y” errors in model validation and calibration, and their implications on extrapolative prediction. Type X error is non-detection of model bias because it is effectively hidden by the uncertainty in the experiments. Possible deleterious effects of Type X error can be avoided by mapping uncertainty into the model until it envelopes the potential model bias, but this likely assigns a larger uncertainty than is needed to account for the actual bias (Type Y error). A philosophy of Best Estimate + Uncertainty modeling and prediction is probably best supported by taking the conservative choice of guarding against Type X error while accepting the downside of incurring Type Y error. An associated methodology involving data- and model- conditioning is presented and tested on a simple but rich test problem.