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.

All models are approximations. Essentially, all models are wrong, but some are useful. However, the approximate nature of the model must always be borne in mind.

The business of the statistician is to catalyze the scientific learning process.

To find out what happens to a system when you interfere with it you have to interfere with it (not just passively observe it).

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.

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.