An approximate answer to the right problem is worth a good deal more than an exact answer to an approximate problem.
The greatest value of a picture is when it forces us to notice what we never expected to see.
The best thing about being a statistician is that you get to play in everyone's backyard.
There is no data that can be displayed in a pie chart, that cannot be displayed BETTER in some other type of chart.
The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.
This is my favorite part about analytics: Taking boring flat data and bringing it to life through visualization.
It's better to solve the right problem approximately than to solve the wrong problem exactly.
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.
Be approximately right rather than exactly wrong.
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.
Numerical quantities focus on expected values, graphical summaries on unexpected values.
When communicating results to non-technical types there is nothing better than a clear visualization to make your point.
In a world in which the price of calculation continues to decrease rapidly, but the price of theorem proving continues to hold steady or increase, elementary economics indicates that we ought to spend a larger and larger fraction of our time on calculation.
All we know about the world teaches us that the effects of A and B are always different-in some decimal place-for any A and B. Thus asking "are the effects different?" is foolish.
To be able to say that "if we change our point of view in the following way ... things are simpler" is always a gain.
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.
If we are going to make a mark (key 21), it might as well be a meaningful one.
I know of no person or group that is taking nearly adequate advantage of the graphical potentialities of the computer.
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