Some statistically based initiatives aimed at solving engineering and quality problems often tend to over-emphasise empirical methods at the expense of deductive logic; the Six Sigma movement is a good example of this – the problem solving algorithm of Define, Measure, Analyze, Improve, and Control (see for example http://en.wikipedia.org/wiki/Six_Sigma for some background to what these steps entail) puts great store in solving problems by measuring lots of characteristics, and analysing the resulting data. However, Six Sigma has nothing to say about eliminating hypotheses through deductive logic. In my Brighton talk, I will introduce a simple method to facilitate this step in problem solving and root cause determination, so that an empirical approach using statistical methods can then be better targeted. This method is not taught in Six Sigma classes (groping around in Minitab output for “significant” p-values seems to be the preferred approach), or even referenced in statistical texts; which is strange given the central role of statistical methods generally in problem solving.

Tim Davis

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