Numerical quantities focus on expected values, graphical summaries on unexpected values.

Far better an approximate answer to the right question, which is often vague, than the exact answer to the wrong question, which can always be made precise.

Visualization is often used for evil - twisting insignificant data changes and making them look meaningful. Don't do that crap if you want to be my friend. Present results clearly and honestly. If something isn't working - those reviewing results need to know.

The best thing about being a statistician is that you get to play in everyone's backyard.

In a single sentence the moral is: admit that complexity always increases, first from the model you fit to the data, thence to the model you use to think about and plan about the experiment and its analysis, and thence to the true situation.

Be approximately right rather than exactly wrong.

To be able to say that "if we change our point of view in the following way ... things are simpler" is always a gain.