Statistical experimental design has the aim of identifying and quantifying the relationship between a dependent variable and one or more independent variables. Constraints on resources and time often prohibit collecting data for all possible variable settings, so selecting a subset from the possibilities becomes a necessity. By defining a metric on the design space, one can use distance to define a "space-filling" criterion which can be used to select an appropriate subset for an experiment. Space-filling designs are particularly desirable in computer experimentation where the models are often quite complex.

Coding theory and design theory share many concepts. The space-filling design problem will be linked to coding theory, and the results of this link will be presented. The results include the identification of several families of space-filling designs as well as a catalog of space-filling designs for up to 27 independent factors (where previous work covered only up to 7 independent factors).