I mean, you might as well say that, like, public health is useless because some people aren't going to quit smoking. But I want to close with an observation of Adam Cifu, another former EconTalk guest, who, in a book, co-authored book, called Medical Reversal, he made the observation that many, many, many multivariate analyses and studies which show statistical significance of some new technique, or new device--when then put into a randomized control trial, do not hold up. take that. Because, as you say, hundreds of studies found the existence of priming once they knew to look for it. This means that using the advertised sampling distribution will not fix alpha at 0.05. As someone who lives north of the 49th parallel, this observation is enough to make me skeptical of our government’s vocal commitment to “evidence-based policy”, as refreshing as it sounds. A group that free market economist and think tanks love. Additional ideas and people mentioned in this podcast episode: A few more readings and background resources: Enter your email address to subscribe to our monthly newsletter: Russ Roberts: My guest is author and blogger, Andrew Gelman, professor of statistics and political science at Columbia University. In other words, multiple different statistical hypotheses can appear supported by theory and so seem reasonable. I assume. Very few people are dentists. Just because you can point to differences of opinions or past or current controversies doesn’t diminish the scientific value of anything. It's the garden of the forking paths. For sure. Like, experts have flaws, but presumably people who know about curricula and child development could do a better job. His blog has influenced me so much, and it’s a big reason why I changed careers and am now completing a Masters degree in Statistics in my free time. Baseball is a controlled environment: so, you can actually measure pretty accurately, either through simulation or through actual data analysis, say, whether trying to steal a base is a good idea. Because it's always being compared to other potential uses of tax dollars. So, in economics, you have: Get your identification. Indeed. There appear to be a couple different ways Gelman sees this problem occurring, which might be part of your confusion. The answer is to distinguish between exploratory data analysis and confirmatory data analysis. And if it turns out that Bob and Bill come to the same conclusion, you've shown that your a priori judgment about that assumption is not too important to arriving at the conclusion. They try something else. Russ Roberts: I think the challenge is, is that baseball is very different from, say, the economy. So, the statement goes as follows: Suppose that the treatment had no effect. No harm in that but wait. Last time I took economics, was in 11th grade. There are all sorts of things it can do. Both were excellent interviews on this fascinating topic! And with care, they could be put together, and put into a larger model. Don't misunderstand me. Go with the ESP first. If they didn't think the effect was large, they would have no motivation to exaggerate it. Just that consistent effects, the consistent effect of something like priming, it's just the average of all local effects. Gelman discusses how this phenomenon is rooted in the incentives built into human nature and the publication process. I think it's possible to fill in the gaps a little bit. Great discussion of some of the fundamental issues of science and “what we know.” One thing I wish had been discussed more is why .05 is the threshold, and what might happen if that threshold was moved to a different level, say .005. Or even 30,000 ways to have performed the analysis. I do work in toxicology and pharmacology, where we have fairly specific models. And they have this noise problem. It doesn't seem consistent with my model, in which nothing's going on.'. I do[?] If white was, they're picking a color that downplays their ovulation (study, 2013). So, what we're observing is a non-random sample of the results measuring this impact. – Thank you for introducing me to Dr. Gelman’s blog, it is very entertaining, even for a dog person (although I must admit that I may not visit it much as my wife says that if I get any geekier we will never be invited to any more dinner parties). | We were taught that this is the way empirical work gets done. When researchers get results that conflict or even contradict their previous work or widely held biases, the study disappears into a drawer never to see the light of a journal. And it will help some people and hurt other people. Click on the link, insert the password provided in the invitation mail, and you will join the call. Andrew Gelman: said he, with his own study that he was ready to publish with his collaborators; and they replicated it; and it didn't replicate. If you look at the paper carefully, it had so many forking paths: there's so much p-hacking--almost every paragraph in the results section--they try one thing, it doesn't work. Also, the construction of social experiments are very loose compared to physics, as discussed. This seems to me quite distinct from the use of statistical analysis. Some of this is due to the expense, but I feel that most is due to the lack of academic prestige associated with “merely” validating other work. The current paradigm of internal versus external validity is a formal manifestation of this general tendency. Because I know it's noisy and full of problems, and has probably been p-hacked. Russ Roberts: So, what's wrong with that conclusion? Center for Open Science Maybe you should disclose this when you discuss science. What worked in Jamaica 25 years ago might not work in Jamaica now, let alone the United States right now. Russ Roberts: It's hard to get a large number where the other things are constant. 2 months ago # … I'm going to rely on basic economic logic, the incentives that I've seen work over and over and over again. This is a form of counterfactual, as applied to null hypothesis significance testing (NHST). Status Consider the type I error rate of a hypothesis test. But this is kind of what everybody's doing. That’s 500M doses of statins to save ONE life. Russ Roberts: And the intervention was when they were 4 years old--. Current AGW forecasts must be accurate (with NO revisions) for several centuries before one can even begin to rule out natural variations (the statistical noise in this week’s podcast). For anything but depending on who you are, it might really tick you off; it might remind you that you have to go the bathroom and you have to walk faster. You say that you are looking for balance. But the alternative (going “with your gut” without even analyzing the data) is sure to be worse. Russ Roberts: So, I want to bring up the Priming example, because I want to make sure we get to it. They could just report that their hypothesis was wrong, right? They are, I think, actually, nicer than I am. Although they don't go into it in the article, the essence of the problem is that NHST violates the likelihood principle. Technology advances (boosted by the harder sciences) while the social sciences remain mired in debates. So, I don't think you should put yourself in a position of having to decide, 'Does it work or not?' And considering the failure of the initial twenty years of long term forecasts, they don’t inspire confidence. Like, the Priming--I don't think, what are you supposed to do with the priming? And I'm right. It's not at all a surprise that they could have got statistical significance. Now, is that still the case? So, however that's done, some approach is chosen. Creationism is not science. Based on my own recent experiences, I think that some of your introspection about your training may be fairly unrepresentative of the way things are taught these days. I will not argue that the predictive power of, say, psychology, epidemiology or even biochemistry is in any way comparable to that of particle physics, but the vision of “science” that social sciences are held up against is one derived from high school physics myths about great scientists sitting down, writing down equations, and then testing them under perfectly controlled conditions.

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