Comments on: Visualization, modeling, and surprises

By: Visual Revelations, Howard Wainer | Civil Statistician

Visual Revelations, Howard Wainer | Civil Statistician — Fri, 22 Mar 2013 02:05:34 +0000

[...] p. 80: “a reasonable strategy in what ought to be an iterative process. Sometimes one has a data-related question and then draws a graph to try to answer it. After drawing the graph a new question might suggest itself, and hence a different graph, better suited to this new question (perhaps with additional data), is drawn. This in turn suggests something else, and so on, until either the data or the grapher is exhausted. [...] My experience suggests that if you begin with a general-purpose plot there is a greater chance of finding what you had not expected.” This is my experience as well, and reminds me also of Hadley Wickham’s description of statistics as iterating between models and graphics. [...]

By: Andrew Gelman

Andrew Gelman — Sat, 09 Feb 2013 19:27:33 +0000

John:

Yup. As Bayes once said, with great power comes great responsibility.

By: John

John — Sat, 09 Feb 2013 17:32:33 +0000

Andrew: I agree, and too often step 3 is missing.

Statistics without model checking makes me uneasy, to put it mildly. Some argue that model checking is less important in Bayesian statistics, but I don’t buy that. If anything, because Bayesian analysis makes it easier to construct complex models, there may be more need for model checking.

By: Andrew Gelman

Andrew Gelman — Sat, 09 Feb 2013 16:24:01 +0000

John:

Yes, this is what we are trying to get at in Bayesian Data Analysis. You iterate the following 3 steps: (1) model building, (2) inference conditional on the model, (3) model checking. The better you do (1) and (2), the more informative step (3) will be.

The paradox, if there is one, is that people tend to think of steps 2 and 3 as competing: in step 2 you (temporarily) commit to a belief, whereas in step 3 you look for problems. I think these go together–really, that’s what the scientific method is all about–but I’ve found that, in many cases, people who spend a lot of time with a model don’t want to check it, while people who spend a lot of time on exploratory data analysis don’t like models at all.

By: Rick Minerich

Rick Minerich — Fri, 08 Feb 2013 04:58:29 +0000

Have you ever looked at Structure Learning for extending models? Very cool stuff. Daphne Koller used it to find new cell protein interactions.