Statistics Tip of the Week: Designed Experiments provide strong evidence of cause and effect.

 For a Process output, y, which is a function of several Factors (x's), that is, for

the Design of Experiments (DOE) discipline can design the most efficient and 
effective experiments to determine the values of the x's which produce the optimal value for -- or the minimal Variation in -- the Response Variable, y.
 
DOE is active and controlling. (This can be done with Processes, but usually not with Populations).
 DOE doesn’t collect or measure existing data with pre-existing values for y and the x’s. DOE specifies Combinations of values for inputs (Factors) and then measures the resulting values of the outputs (Responses). This is the Design of the Experiment.
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Statistical software packages perform DOE calculations which specify the elements which make up the Design:

• Levels (e.g., high and low values) of the x Variables
• Combinations of variables and values
• Replications
• Order

Designed Experiments (those designed by DOE) provide much stronger evidence of Cause and Effect than Inferential Statistics. If a Regression Model is to be a valid model of Cause and Effect, it must be able to predict future data derived from controlled experiments. Experiments designed by DOE are a good way to test this.