For the theory-practice iteration to work, the scientist must be, as it were, mentally ambidextrous; fascinated equally on the one hand by possible meanings, theories, and tentative models to be induced from data and the practical reality of the real world, and on the other with the factual implications deducible from tentative theories, models and hypotheses.

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

Discovering the unexpected is more important than confirming the known.

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.

The only way to know how a complex system will behave-after you modify it-is to modify it and see how it behaves.

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.

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