A rather recent trend in visualization is to combine traditional scientific visualization methods (such as volume rendering) with information and statistical visualization techniques (such as scatterplots). This combination of techniques is particularly useful for multi-variate data defined on spatial grids because the spatial relationships and the characteristics of the data attributes can be shown simultaneously. However; statistical and information visualization methods for multi-variate data; such as scatterplots and parallel-coordinates plots; have traditionally been applied to intrinsically discrete data points and; therefore; treat data as a collection of independent data samples. In contrast; this talk advocates the use of continuous data models for statistical visualization applied to continuous data. Main advantages are that visual artifacts from data sampling are avoided and that the visualization process becomes scalable with respect to data set size.