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What is Statistical Design of Experiments (DOE)?

You can collect scientific data from two very basically different ways: first being through passive observation such as making astronomical observations or weather measurements or earthquake seismometer readings, and second, through active experimentation.

The passively awaiting informative events or data are essential for determining how Nature works and serve both as raw material from which new models – that is, new theories – are built and as evidence for or against existing ones.

Active experimentation on the other hand, is powerful in bringing up a physical relationship between observed phenomena and the contributing causes. You are able to manipulate and evaluate the effects of possibly many different inputs, commonly referred to as experimental factors. But, experimentation can be expensive and time consuming, and experimental ‘noise’ can make it difficult of clearly understand and interpret what results.

The classical experimental approach is to study each experimental factor separately. This one-factor-at-a-time (OFAT) strategy is easy to handle and widely employed.  But, we all know, this is not the most efficient way to approach an experimental problem when you have many variable factors to be considered.

Why?

Remember that the goal of any experiment is to obtain clear, reproducible results and establish general scientific validity. When you have a study which involves a large number of variables and each experiment on a variable while making the other variables constant can be time consuming. Moreover we cannot afford to run large numbers of trials due to budget constraints and other considerations.

Hence, it is important to develop a revolutionary approach with a statistical plan that guarantees experimenters or researchers an optimal research strategy. This is to help them to obtain better information faster and with less experimental effort. Design of experiments (DOE) therefore is a planned approach for determining cause and effect relationships. It can be applied to any process or experiment with measurable inputs and outputs.

To appreciate the power of DOE, let’s learn some of its historical background.

DOE was first developed for agricultural purposes. Agronomists were the first scientists to confront the problem of organizing their experiments to reduce the number of trials in the field. Their studies invariably include a large number of factors (parameters), such as soil composition, fertilizers’ effect, sunlight available, ambient temperature, wind exposure, rainfall rate, species studied, etc., and each experiment tends to last a long time before seeing and evaluating the results.

At the beginning of the 20^th century, Fisher first proposed methods for organizing trials so that a combination of factors could be studied at the same time. These were the Latin Square, analysis of variance, etc. The ideas of Fisher were subsequently taken up by many well known agronomists such as Yates and Cochran and by statisticians such as Plackett and Burman, Youden, etc.

During World War II and thereafter, it became a tool for quality improvement, along with statistical process control (SPC). The concepts of DOE was used to develop powerful methods which were found useful amongst major industrial companies such as Du Pont de Nemours, ICI in England and TOTAL in France. They began using experimental designs in their laboratories to speedily improve their research activities on the products investigated.

Until 1980, DOE was mainly used in the process industries (i.e. chemical, food, pharmaceutical) mainly because of the ease with which the engineers could manipulate factors, such as time, temperature, pressure and flow rate.  Then, stimulated by the tremendous success of Japanese electronics and automobiles, SPC and DOE underwent a renaissance. Today, the advent of personal computers further catalyzed the use of these numerically intense methods.