Rob Gould reports on what appears to have been interesting panel discussion on the future of statistics hosted by the UCLA Statistics Department. The panelists were Songchun Zhu (UCLA Statistics), Susan Paddock (RAND Corp.), and Jan de Leeuw (UCLA Statistics).
He describes Jan’s thoughts on the future of inference in the field of statistics:
Jan said that inference as an activity belongs in the substantive field that raised the problem. Statisticians should not do inference. Statisticians might, he said, design tools to help specialists have an easier time doing inference. But the inferential act itself requires intimate substantive knowledge, and so the statistician can assist, but not do.
I found this comment to be thought provoking. First of all, it sounds exactly like something Jan would say, which makes me smile. In principle, I agree with the premise. In order to make a reasonable (or intelligible) inference you have to have some knowledge of the substantive field. I don’t think that’s too controversial. However, I think it’s incredibly short-sighted to conclude therefore that statisticians should not be engaged in inference. To me, it seems more logical that statisticians should go learn some science. After all, we keep telling the scientists to learn some statistics.
In my experience, it’s not so easy to draw a clean line between the person analyzing the data and the person drawing the inferences. It’s generally not possible to say to someone, “Hey, I just analyze the data, I don’t care about your science.” For starters, that tends to make for bad collaborations. But more importantly, that kind of attitude assumes that you can effectively analyze the data without any substantive knowledge. That you can just “crunch the numbers” and produce a useful product.
Ultimately, I can see how statisticians would want to stay away from the inference business. That part is hard, it’s controversial, it involves messy details about sampling, and opens one up to criticism. And statisticians love to criticize other people. Why would anyone want to get mixed up with that? This is why machine learning is so attractive–it’s all about prediction and in-sample learning.comments powered by Disqus