Experimental Design - Experiments in Science and Industry
Experimental methods are widely used in research as well as in industrial settings, however, sometimes for very different purposes. The primary goal in scientific research is usually to show the statistical significance of an effect that a particular factor exerts on the dependent variable of interest (for details concerning the concept of statistical significance see Elementary concepts).
In industrial settings, the primary goal is usually to extract the maximum amount of unbiased information regarding the factors affecting a production process from as few (costly) observations as possible. While in the former application (in science) analysis of variance (ANOVA) techniques are used to uncover the interactive nature of reality, as manifested in higher-order interactions of factors, in industrial settings interaction effects are often regarded as a "nuisance" (they are often of no interest; they only complicate the process of identifying important factors).
Differences in techniques
These differences in purpose have a profound effect on the techniques that are used in the two settings. If you review a standard ANOVA text for the sciences, for example the classic texts by Winer (1962) or Keppel (1982), you will find that they will primarily discuss designs with up to, perhaps, five factors (designs with more than six factors are usually impractical; see Introductory Overview to ANOVA/MANOVA). The focus of these discussions is how to derive valid and robust statistical significance tests. However, if you review standard texts on experimentation in industry (Box, Hunter, and Hunter, 1978; Box and Draper, 1987; Mason, Gunst, and Hess, 1989; Taguchi, 1987) you will find that they will primarily discuss designs with many factors (e.g., 16 or 32) in which interaction effects cannot be evaluated, and the primary focus of the discussion is how to derive unbiased main effect (and, perhaps, two-way interaction) estimates with a minimum number of observations.
This comparison can be expanded further, however, a more detailed description of experimental design in industry will now be discussed and other differences will become clear.
Note that the ANOVAMANOVA help contains detailed discussions of typical design issues in scientific research, and the ANOVA/MANOVA procedure is a very comprehensive implementation of the general linear model approach to ANOVA/MANOVA (univariate and multivariate ANOVA). There are of course applications in industry where general ANOVA designs, as used in scientific research, can be immensely useful. You may want to read the Introductory Overview section to ANOVA/MANOVA to gain a more general appreciation of the range of methods encompassed by the term Experimental Design.