Once the first systematic campaign is completed, it is usual to proceed to estimations of global in situ resources, such as tonnages of ore and overburden, quantities of metals and mean grades. These estimations must also include confidence intervals which will determine the point at which the in situ resources are sufficiently well estimated to proceed to the next stage of evaluation, i.e., that of the recoverable reserves. At this stage of global evaluation of resources, there are no specific geostatistical methods for determining estimators. For example, a mineralized surface may be estimated by interpolation between positive and negative drill-holes. Mineralized, thickness or mean grade may be estimated by the arithmetic mean of the positive data on a pseudo-regular grid, or by any of the traditional methods of extension of profiles or cross-sections. However, through the estimation variance, geostatistics can quantify the reliability of each of these traditional methods and so provide a criterion for choice between them. The estimation variance can also be used to objectively classify resources and to predict the number, location and distribution of additional information required to improve the quality of estimations, e.g., to move a given tonnage from the ``possible'' to the ``proved'' category, cf. A. G. Royle (1977).