  Successful predictions are among the most compelling validations of any
model. Extracting falsifiable predictions from nonlinear multiparameter models
is complicated by the fact that such models are commonly sloppy, possessing
sensitivities to different parameter combinations that range over many decades.
Here we discuss how sloppiness affects the sorts of data that best constrain
model predictions, makes linear uncertainty approximations dangerous, and
introduces computational difficulties in Monte-Carlo uncertainty analysis. We
also present a useful test problem and suggest refinements to the standards by
which models are communicated.
