DESIGN OF EXPERIMENTS
Well designed experiments can reduce the risk of coming to an incorrect conclusion during a process characterization, assay
validation, or process validation study. The primary goal is usually to extract the maximum amount of information regarding
the factors from as few observations as possible. Typically, design of experiments can be categorized into two classes: screening
designs and optimization designs. Screening designs are smaller sets of experiments that are intended to identify the critical
few factors from the many potential trivial factors. A screening design assumes a linear effect, usually at two different
levels or settings of the factor. Typical screening designs are called fractional factorial or Plackett-Burman designs. Optimization designs, sometimes called response surface designs, are larger experiments that investigate interactions of terms and nonlinear responses, and are conducted at more
than two levels for each factor. Typical optimization designs are called central composite or Box-Behnken.
The data from a well designed experiment can be used to model the response as a function of the different factors. Regression
methods as discussed previously can be applied to these data. Analysis of variance (ANOVA) is the statistical test used to
assess differences in factor levels. The basis for ANOVA is the variability between factor levels compared with the average
variability within a factor level.
A well designed experiment makes it easier to understand different sources of variation. Analysis techniques such as regression
and ANOVA help to partition the variation for predicting the response or determining if the differences seen between factor
levels are more than expected when compared to the variability seen within a factor level. This paper just scratches the surface
of the various statistical techniques available to the researcher or scientist. A more comprehensive course in statistics
will help clarify the differences in the methods.
1. Anscombe FJ. Graphs in Statistical Analysis. American Statistician, 27, 17–21.
Steven Walfish is the president of Statistical Outsourcing Services, Olney, MD, 301.325.3129, firstname.lastname@example.org