to Keith:
My favorite professor in grad school said that he noticed a trend. People who knew very little statistics used simple methods, because they knew nothing else. But they used them incorrectly. People who knew a little more statistics used fancy methods, but used them incorrectly, when simple methods would do. And, he said, people who knew a lot about statistics often used simple methods, but did it right
Right now I think the best journals for simple statistical methods are Technometrics, Statistics in Medicine, and the American Statistician. If there's a bias against simple methods in other journals, I'm perhaps partly to blame, as I'm on the editorial boards of several of them.
One of the difficulties is that, although I like simple statistical methods, and I like _my_ new simple statistical methods (that's what I'll speak on next month), I don't always like the new methods that _other people_ develop. In grad school we joked that every issue of the American Statistician featured a new proof that the median is between the mean and the mode, or some new criterion for outliers. That's the kind of simple method I can do without.
More generally, I think what is needed is bridging between simple and complex methods, so that when they differ, we understand how this happened. My impression is that this used to be a standard feature in statistical analysis (for example, the "partial correlation coefficient") but has gone away. We do some of this bridging in our recent Jasa article on police stop-and-frisk, and we discussed it a bit in our recent book on regression and multilevel models, but I think there's room for more research on the topic.
Sounds good
Do we just show up, or do we have to register or something?
You can just show up, bringing along your best anecdotes.
"Some Recent Progress in Simple Statistical Methods"
Suspect the strong bias against simple methods in applied statistics journals is still there - but is that changing somewhat?
Keith
to Keith:
My favorite professor in grad school said that he noticed a trend. People who knew very little statistics used simple methods, because they knew nothing else. But they used them incorrectly. People who knew a little more statistics used fancy methods, but used them incorrectly, when simple methods would do. And, he said, people who knew a lot about statistics often used simple methods, but did it right
Keith,
Right now I think the best journals for simple statistical methods are Technometrics, Statistics in Medicine, and the American Statistician. If there's a bias against simple methods in other journals, I'm perhaps partly to blame, as I'm on the editorial boards of several of them.
One of the difficulties is that, although I like simple statistical methods, and I like _my_ new simple statistical methods (that's what I'll speak on next month), I don't always like the new methods that _other people_ develop. In grad school we joked that every issue of the American Statistician featured a new proof that the median is between the mean and the mode, or some new criterion for outliers. That's the kind of simple method I can do without.
More generally, I think what is needed is bridging between simple and complex methods, so that when they differ, we understand how this happened. My impression is that this used to be a standard feature in statistical analysis (for example, the "partial correlation coefficient") but has gone away. We do some of this bridging in our recent Jasa article on police stop-and-frisk, and we discussed it a bit in our recent book on regression and multilevel models, but I think there's room for more research on the topic.