At the heart of the ever popular Six Sigma methodology lies the successful element of systematic approaches to providing solutions to problems while at the same time focusing on the impact it makes on the many customers. In six sigma, statistical tools and analysis are part and parcel of the entire process. Then again, one must always take caution when it comes to looking at statistics to be the heart and soul of Six Sigma methodology, because it involves so much more. A proper six sigma project can be launched using very rudimentary statistical tools.
Unfortunately, a lot of professional statisticians in today’s world tend to overly criticize the effectiveness of statistics as applied to six sigma due to the fact that they have highly varied perspectives when it comes to the understanding the statistics that are involved in the project. The different statistical methods that are employed in six sigma have varying differences compared to those that are taught in other engineering or statistical programs. Statistics as applied to six sigma is more of the observational and experimental scientific context like two-level factorial analyses (a standard for statistics) an analysis-helping graphical methods as well. It must be emphasized that experimentation cannot be likened to a simple analysis of variance. Sure, it can definitely facilitate a type of understanding of some results that came out of experimentation for most statisticians, but for others (like engineers) it can serve as an impediment when it comes to planning for as well as carrying out different types of experiments.