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Experimental design is a statistical technique used to optimize processes. The effect of process variables, like time, temperature and pressure, are studied by setting them at two or more levels and running tests with various combinations of these variables. The combinations used are set using statistical design techniques and the results analyzed statistically. The end result of the study shows which variables are major and which are minor.

As an example, if one wanted to design a recipe for a cake, you might want to study the effect of bake time and oven temperature. By baking several cakes at long and short times and high and low temperatures and studying the quality of the finished cakes you can decide what combination of time and temperature is best. Once an initial direction is established, then the prime variables can be studied and optimized.

This is an oversimplified example that would only need four tests: high-high, high-low, low-high and low-low. You can quickly see that if we were to add more variables, like one or two eggs and oil or butter, the number of tests quickly becomes overwhelming. Experimental design allows the elimination of many combinations that would provide duplicate or meaningless data. The number of tests can be shrunk form hundreds to only a relative few.

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