Sediment site managers are often confronted with decisions on sampling density to properly capture spatial variability for site characterization, and with decisions regarding the scale of new technology demonstrations to enable efficacy assessment. Reliable characterization of the spatial distribution of sediment site attributes, such as contaminant concentrations, the impact of microbial activity on contaminants, and microbial characteristics depends on how well sampled values represent all values throughout the entire site. Whereas geostatistical tools have been developed to interpolate the attribute values in space, these do not explicitly take into account the uncertainties associated with the various scales (field cores, columns, or microcosms) at which the data have been collected. A recently developed statistical model (M-Scale) takes into account multiple scales and multiple resolutions to optimize the reliability of sampled data. The model not only serves as a tool to evaluate parameter relationships over different scales by their covariances and data uncertainty, but also makes further use of these covariances and data uncertainty as basis for a precision-optimized estimator. These estimators can then be used to scale laboratory information to the field, and conversely, to use field-derived data for uncertainty-based decision-making for technology demonstrations. Information from each scale will be weighted by the projected similarity to the scales of interest, with adjustments considering the different precision they provide. Unlike conventional geostatistic tools that are based on the point-to-point spatial structures, the multi-scale model introduces a new framework for spatial analysis in which regional values at different scales are anchored by the correlations of each other. Examples will be presented using dioxin distributions, the impact of microbial dechlorination activity on the patterns observed, and microbial abundance interpolations.