Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.

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.

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

Discovering the unexpected is more important than confirming the known.

The old-fashioned idea of a good manager is one who is supposed to know all the answers, can solve every problem himself, and can give appropriate orders to his subordinates to carry out his plans... A good modern manager is like a good coach who leads and encourages his team in never-ending quality improvement.

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.

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.