Pretty as a Picture

Along with an interest in stats and graphs comes a level of responsibility. Kind of, guns don't kill people, people kill people. The canonical text is "How to Lie With Statistics", which was first published in 1954. Legend has it, it's never been out of print. Likely so.

It so happens that I've found a couple of blogs/sites which both deal with graphing stat data in non-disinterested ways. I'll note once again that a stat/quant/analyst/foobar is supposed to be disinterested. S/he's just an impartial judge of the data, trying to scope out the real relationships in the data; if there are any, there may not be. Data associated with politics is particularly susceptible to bias. But others face the same pressure. Worker stat bees (having been there) are often encouraged to slant the presentation in a way to make the nappie marketing Suits look like geniuses. It's a problem everywhere; all worker bees are expected to behave as attorneys; staunch defenders of whatever the Suits have done.

Watching the response to drug clinical trials is particularly amusing. Rather often, the sponsor will be shocked (shocked, I say) that its new FooBar Resolver didn't blast the .05 requirement out of the water. There'll be "unexpected placebo levels" or "unbalanced randomization" or "the FooBar Resolver patients were sicker than placebo". And so on.

Be that as it may, here are a couple of sites worth grazing:
The R Graph Gallery, from Romain François
The Gallery of Data Visualization, from Michael Friendly