Future quantitative assessments will be expected to estimate quantities, values, and locations of undiscovered mineral resources in a form that conveys both economic viability and uncertainty associated with the resources. Historically, declining metal prices point to the need for larger deposits over time. Sensitivity analysis demonstrates that the greatest opportunity for reducing uncertainty in assessments lies in lowering uncertainty associated with tonnage estimates. Of all errors possible in assessments, those affecting tonnage estimates are by far the most important. Selecting the correct deposit model is the most important way of controlling errors because the dominance of tonnage-deposit models are the best known predictor of tonnage. Much of the surface is covered with apparently barren rocks and sediments in many large regions. Because many exposed mineral deposits are believed to have been found, a prime concern is the presence of possible mineralized rock under cover. Assessments of areas with resources under cover must rely on extrapolation from surrounding areas, new geologic maps of rocks under cover, or analogy with other well-explored areas that can be considered training tracts. Cover has a profound effect on uncertainty and on methods and procedures of assessments because geology is seldom known and geophysical methods typically have attenuated responses. Many earlier assessment methods were based on relationships of geochemical and geophysical variables to deposits learned from deposits exposed on the surface-these will need to be relearned based on covered deposits. Mineral-deposit models are important in quantitative resource assessments for two reasons: (1) grades and tonnages of most deposit types are significantly different, and (2) deposit types are present in different geologic settings that can be identified from geologic maps. Mineral-deposit models are the keystone in combining the diverse geoscience information on geology, mineral occurrences, geophysics, and geochemistry used in resource assessments and mineral exploration. Grade and tonnage models and development of quantitative descriptive, economic, and deposit density models will help reduce the uncertainty of these new assessments.