Just as the ability to devise simple but evocative models is the signature of the great scientist so overelaboration and overparameterization is often the mark of mediocrity.
All models are wrong, but some are useful.
Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.
Discovering the unexpected is more important than confirming the known.
The only way to know how a complex system will behave-after you modify it-is to modify it and see how it behaves.
Statisticians, like artists, have the bad habit of falling in love with their models.
The management system which makes only a pretense of valuing employee involvement and encouraging employee empowerment merely breeds cynicism.
The benefits provided by worker participation are twofold. Quality is improved because of the finding and fixing of a very large number of problems, but also, and perhaps equally important, moral is improved.
The business of the statistician is to catalyze the scientific learning process.
Management must provide employees with tools that will enable them to do their jobs better, and with encouragement to use these tools. In particular, they must collect data.
One important idea is that science is a means whereby learning is achieved, not by mere theoretical speculation on the one hand, nor by the undirected accumulation of practical facts on the other, but rather by a motivated iteration between theory and practice.