Statistical experiment design can be used to efficiently select experimental runs to achieve a given experimental purpose. However, uncertainty is a fact of life in experimentation. The experimenter is faced with uncertainty in inputs, uncertainty in outputs from both random variability and uncertainty in measurement processes, as well as uncertainty about the underlying model structure of the phenomenon under investigation. In the face of all this uncertainty, the experimenter must try to collect and analyze data that will address questions of scientific interest.
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Joanne Wendelberger (2010) studied this question.
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