Use of Statistical Process Control (SPC) Versus Traditional Statistical Methods in Dow Corning Personal Care Applications

The basis for traditional “statistical inference” is the assumption of statistical stability. However, observations indicate that this assumption is often incorrect and has significant limitations for the study of skin and hair systems. This paper focuses on the use of analytic statistical analysis versus enumerative statistical analysis for evaluating data from experiments and product studies. Traditional statistical analysis depends on the experimenter to classify random variation (i.e., experimental noise) based on experience. This usually results in an inflated estimate of noise level, which can obscure valuable signals from the study. W. A. Shewhart developed control charts to evaluate data taken during analytic studies; these charts represent a powerful tool for gaining an understanding of experimental variation and results when compared to methods of statistical inference. Analysis of experimental data using the methods of statistical process control (SPC) allows the assumption of statistical stability to be tested. Further, the graphical techniques of SPC allow the experimenter to “see” data, allowing insight into cause-and-effect relationships not readily apparent with traditional statistical methods. SPC techniques allow discovery of sources of non-random variation, resulting in a better understanding of the system being studied.