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From a Bayesian perspective, you should have had a high prior that lifting weights will cause an in increase your longevity because of several plausible mechani

by bjterry 14y ago

From a Bayesian perspective, you should have had a high prior that lifting weights will cause an in increase your longevity because of several plausible mechanisms, and this study should slightly increase your posterior probability for that causation. Limiting your understanding of the causations of things only to those things which have had interventionist studies performed on them will have a very deleterious effect on your life, with high probability. No one has performed an interventionist study on basically anything in macroeconomics, politics, relationships, English literature, history or just about anything else, so I hope that you apply the same understanding to all of those fields as you do to the field of health and simply throw your hands up in the air when asked if "cheating causes divorces" or "yelling racial epithets causes a lack of re-election." And please don't After Edit me, I find it patronizing.

micro_cam 14y ago
While this is an application of Bayes' theorem it isn't what a statistician would consider a good Bayesian analysis. Nate Silver's book and xkcd have confused this issue but a bayesian does not generally make strong assumptions about a prior distribution. A Bayesian can make strong assumptions about the hierarchical structure of probability distributions linking the paramaters/priors to the observations. Frequently she will then use uninformative prior distributions (like a really broad normall) on the paramaters of interest to avoid confirmation bias and combat overfitting when doing inference. The debate between modern frequentists (like Larry Wassermann whose blog I linked) and bayesians (like Andrew Gelman) has more to do with weather analysis done with an assumed hierarchical model is a good idea. Everyone recognizes that bayes' theorem is valid and priors are a usable tool at this point.
- bjterry 14y ago
  I understand that in the context of professional applications of statistics, such as when writing research papers, professional statisticians often use uninformative priors. I'm not talking about professional statistics, and I'm not talking about the supposed debate between "frequentists" and "Bayesians." I'm talking about how an individual lives their life, sample size: one. The Earth doesn't have enough resources to answer every question of cause and effect that will come up in your life. For the vast majority of these questions, you will use a three pound mass of mostly fat to determine causation. Teaching that mass of fat that credible mechanisms of action and pre-existing information provides prior context for evaluating newly received information helps it not to discard valuable insight. If you provided me research that showed, weakly, that using racial epithets did not affect a politicians chances of re-election (in relevant countries and scenarios, etc.), I would not discard my existing prior that it does, and I would be right to do so. But I would keep it in mind for the next round of evidence in the event I could be wrong. In this case, no professional statistican is going to perform a Bayesian analysis to tell you the probability that making yourself stronger will cause a decrease in your all-cause mortality. No one is going to aggregate that information and tell YOU what YOU need to do, in this case as in so many others. But there is enough information that you should nonetheless, and this study, like it or not, does add to that information, not detract from it. P.S.: I disagree with using uninformative priors except when they are warranted. This is how alternative medicine masquerades as "Evidence-based Medicine."

by bjterry 14y ago

micro_cam 14y ago

While this is an application of Bayes' theorem it isn't what a statistician would consider a good Bayesian analysis. Nate Silver's book and xkcd have confused this issue but a bayesian does not generally make strong assumptions about a prior distribution. A Bayesian can make strong assumptions about the hierarchical structure of probability distributions linking the paramaters/priors to the observations. Frequently she will then use uninformative prior distributions (like a really broad normall) on the paramaters of interest to avoid confirmation bias and combat overfitting when doing inference. The debate between modern frequentists (like Larry Wassermann whose blog I linked) and bayesians (like Andrew Gelman) has more to do with weather analysis done with an assumed hierarchical model is a good idea. Everyone recognizes that bayes' theorem is valid and priors are a usable tool at this point.

bjterry 14y ago
I understand that in the context of professional applications of statistics, such as when writing research papers, professional statisticians often use uninformative priors. I'm not talking about professional statistics, and I'm not talking about the supposed debate between "frequentists" and "Bayesians." I'm talking about how an individual lives their life, sample size: one. The Earth doesn't have enough resources to answer every question of cause and effect that will come up in your life. For the vast majority of these questions, you will use a three pound mass of mostly fat to determine causation. Teaching that mass of fat that credible mechanisms of action and pre-existing information provides prior context for evaluating newly received information helps it not to discard valuable insight. If you provided me research that showed, weakly, that using racial epithets did not affect a politicians chances of re-election (in relevant countries and scenarios, etc.), I would not discard my existing prior that it does, and I would be right to do so. But I would keep it in mind for the next round of evidence in the event I could be wrong. In this case, no professional statistican is going to perform a Bayesian analysis to tell you the probability that making yourself stronger will cause a decrease in your all-cause mortality. No one is going to aggregate that information and tell YOU what YOU need to do, in this case as in so many others. But there is enough information that you should nonetheless, and this study, like it or not, does add to that information, not detract from it. P.S.: I disagree with using uninformative priors except when they are warranted. This is how alternative medicine masquerades as "Evidence-based Medicine."