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I'm a statistician, and I have to say, I agree with the fundamental premise of the author: The problems with null hypothesis significant testing (NHST) are a d

I'm a statistician, and I have to say, I agree with the fundamental premise of the author: The problems with null hypothesis significant testing (NHST) are a direct result of two things: 1. the false air of objectivity it provides 2. the lack of consideration of the relative likelihood of the data under any alternative I also agree that Pearson, Fisher, and Neyman knew what they were doing when they appealed to these methods' "objectivity" to support their terrible views. I also find it just absolutely hilarious that Fisher's adamant opposition to Bayesian inference was the (in his opinion) absurdity of the uniform prior distribution, and yet one of his most famous contributions (Maximum likelihood estimation) is essentially the same result you'd get from the Bayesian estimate of the most likely parameter with a uniform prior. If anyone is interested in the history of this stuff along with a discussion of the philosophical meaning of probability from the same author, his book Bernoulli's Fallacy is a pretty good read. My only qualm with it is the lack of much exposure on how to actually do Bayesian inference or a discussion on what it's like to do it in a modern setting. There's a brief few pages at the very end that mention things like Markov Chain Monte Carlo and the fact that most modern Bayesian inference is necessarily numerical approximations to the posterior, not the analytical solution (but that's not necessarily a bad thing nor is it an escape-able problem). If you want a book that makes many of the same philosophical arguments about probability but also backs it up with math and descriptions on how to do Bayesian inference, Edwin Jaynes's Probability Theory: The Logic of Science is for you. It's quite literally a modern cult classic. People that read this book tend to go out and evangelize for it. My final comment is that there is a place for frequentist methods, even if you're a modern Bayesian. The theory of Markov Chain Monte Carlo (one of the key methods in making modern Bayesian inference work) is exactly the domain of frequentist probability: Long runs of repeated things. Many of the diagnostic tools for assessing whether the inferences of MCMC results can be trusted are hinged on results from the frequentist interpretation and are steeped in their strange-ness. For instance, the lack of an issue is taken lack of evidence of an issue rather than evidence of no issue. The interconnected-ness of the two is unfortunately missing from the author's work (that I have seen).

dekhn 4y ago
I worked with a guy who is a legit science genius and he read Efron (not Jaynes) and it changed how he thought about statistics, specifically how bayesian bootstrap is a very simple and easy way to get more utility from your data set. https://www.ams.org/journals/bull/2013-50-01/S0273-0979-2012-01374-5/S0273-0979-2012-01374-5.pdf https://www.ams.org/journals/bull/2013-50-01/S0273-0979-2012... Here's something he publisehd on it https://threeplusone.com/pubs/Crooks2005a.pdf https://threeplusone.com/pubs/Crooks2005a.pdf https://threeplusone.com/pubs/Price2005.pdf https://threeplusone.com/pubs/Price2005.pdf
wrp 4y ago
This article combines some issues that I think are really independent, so I'll have to comment by separating them. 1. Some of the founding figures of modern statistics held objectionable moral views. He would like to cancel them for that, but can't because they were such central contributors to the field. OK, I guess nothing more to say about that. 2. There are technical reasons for objecting to NHST. You seem well read on that and I probably agree with your position. 3. Eugenics is bad. However, eugenics is bad because of its moral position, not because of faulty scientific work. The field of animal husbandry is well developed and scientifically sound. Eugenics is the application of the principles of animal husbandry to humans. It is not the science that is necessarily bad, but the motivation of its application. This is not to deny that there may have been poor quality work done in the field of eugenics. 4. Some of the work published in eugenics may have been the result of sloppy or dishonest analysis. This could be true, but again, this is not what makes eugenics bad. Also, the fact that we can evaluate research as being sloppy or dishonest indicates that we do have objective standards for acceptable work.
- JHonaker 4y ago
  I agree that it combines things to try to form a more rhetorically convincing argument, but to me, most of them are convincing on their own. I think it's also more representative of the author's view that he believes that it was precisely the extreme conviction that white, European society was morally, culturally, and genetically superior that led to the development of methods that were devoid of a preference for position (and thus completely objective) that led to many of the confusing methods in use today. I also think there's a useful distinction to be made between the terms "genetics" and "eugenics". I don't think anyone would argue that there aren't meaningful genetic differences between different groups of humans. It's the degree of extremity and the sheer horror of the conclusions drawn and methods advocated for by eugenicists that makes the distinction. Like yes, an increased probability of sickle cell anemia is a direct result of a population's adaption to an environment rife with Malaria, but that doesn't justify (I'm vaguely paraphrasing Pearson or Galton from memory here) the genocide and replacement of those people with those more capable of exploiting the natural resources of their environment (meaning white Victorian aristocrats, or at least slaves "employed" by white, Victorian aristocrats). It's interesting to see conversations around human genomic differences rise up again with things like CRISPR, and I guess that would fall under "eugenics" but it's clearly not the same thing. I also take issue with the idea that "it's not the science that's bad" in even tacit defense of early 1900s eugenicists. It's very telling to read the descriptions of the studies and conclusions that Galton, Pearson, and Fisher performed. It's telling to watch them discount clear counter-examples or re-define the desirability of traits, to watch them call an aristocrat industrious and a Jewish migrant greedy, to watch them all but spell out exactly what their conclusions are, develop methods "devoid of opinion" and of "pure objectivity through data," and then unabashedly call for the justified forced sterilization, mass genocide, and the denigration of non-white, non-aristocratic, non-male, non-Europeans as deterministically inferior. Sorry for the rant. I wasn't aware of the horrific history of statistics when I decided to take it up, and now I take it as a person mission to fight against the "let the data speak for themselves" attitude. I want to make sure people know what "objective in-difference" with implicit assumptions can justify. Maybe more to the point of your points :D : 1. Yea, not much more to say about that. 2. We do agree based on our conversation elsewhere in these threads. 3. It is bad because of both its immoral position and its faulty scientific work. The justification is equivalent to that of any of the latest unrepeatable studies on power posing and finite will power. 4. Again, it was intentionally misleading (as much as someone wholeheartedly convinced of something is misleading), and doesn't stand up to even contemporary criticisms (IMO). I do think we have objective standards, but I don't think those objective standards are reflected or implied by NHST. Nor do I think a modern person using NHST is somehow a eugenicist by transitivity. It's more that my position is NHST has infected the scientific community precisely because of its self-reported objectivity. I think it's my field's (statistics) personal failure to bear. I think we stayed content too long as the dispensers of "objective methods" without really doing a good job of stressing the nuances and frailties associated with any particular method.

dekhn 4y ago

I worked with a guy who is a legit science genius and he read Efron (not Jaynes) and it changed how he thought about statistics, specifically how bayesian bootstrap is a very simple and easy way to get more utility from your data set. https://www.ams.org/journals/bull/2013-50-01/S0273-0979-2012-01374-5/S0273-0979-2012-01374-5.pdf https://www.ams.org/journals/bull/2013-50-01/S0273-0979-2012... Here's something he publisehd on it https://threeplusone.com/pubs/Crooks2005a.pdf https://threeplusone.com/pubs/Crooks2005a.pdf https://threeplusone.com/pubs/Price2005.pdf https://threeplusone.com/pubs/Price2005.pdf

wrp 4y ago

This article combines some issues that I think are really independent, so I'll have to comment by separating them. 1. Some of the founding figures of modern statistics held objectionable moral views. He would like to cancel them for that, but can't because they were such central contributors to the field. OK, I guess nothing more to say about that. 2. There are technical reasons for objecting to NHST. You seem well read on that and I probably agree with your position. 3. Eugenics is bad. However, eugenics is bad because of its moral position, not because of faulty scientific work. The field of animal husbandry is well developed and scientifically sound. Eugenics is the application of the principles of animal husbandry to humans. It is not the science that is necessarily bad, but the motivation of its application. This is not to deny that there may have been poor quality work done in the field of eugenics. 4. Some of the work published in eugenics may have been the result of sloppy or dishonest analysis. This could be true, but again, this is not what makes eugenics bad. Also, the fact that we can evaluate research as being sloppy or dishonest indicates that we do have objective standards for acceptable work.

JHonaker 4y ago
I agree that it combines things to try to form a more rhetorically convincing argument, but to me, most of them are convincing on their own. I think it's also more representative of the author's view that he believes that it was precisely the extreme conviction that white, European society was morally, culturally, and genetically superior that led to the development of methods that were devoid of a preference for position (and thus completely objective) that led to many of the confusing methods in use today. I also think there's a useful distinction to be made between the terms "genetics" and "eugenics". I don't think anyone would argue that there aren't meaningful genetic differences between different groups of humans. It's the degree of extremity and the sheer horror of the conclusions drawn and methods advocated for by eugenicists that makes the distinction. Like yes, an increased probability of sickle cell anemia is a direct result of a population's adaption to an environment rife with Malaria, but that doesn't justify (I'm vaguely paraphrasing Pearson or Galton from memory here) the genocide and replacement of those people with those more capable of exploiting the natural resources of their environment (meaning white Victorian aristocrats, or at least slaves "employed" by white, Victorian aristocrats). It's interesting to see conversations around human genomic differences rise up again with things like CRISPR, and I guess that would fall under "eugenics" but it's clearly not the same thing. I also take issue with the idea that "it's not the science that's bad" in even tacit defense of early 1900s eugenicists. It's very telling to read the descriptions of the studies and conclusions that Galton, Pearson, and Fisher performed. It's telling to watch them discount clear counter-examples or re-define the desirability of traits, to watch them call an aristocrat industrious and a Jewish migrant greedy, to watch them all but spell out exactly what their conclusions are, develop methods "devoid of opinion" and of "pure objectivity through data," and then unabashedly call for the justified forced sterilization, mass genocide, and the denigration of non-white, non-aristocratic, non-male, non-Europeans as deterministically inferior. Sorry for the rant. I wasn't aware of the horrific history of statistics when I decided to take it up, and now I take it as a person mission to fight against the "let the data speak for themselves" attitude. I want to make sure people know what "objective in-difference" with implicit assumptions can justify. Maybe more to the point of your points :D : 1. Yea, not much more to say about that. 2. We do agree based on our conversation elsewhere in these threads. 3. It is bad because of both its immoral position and its faulty scientific work. The justification is equivalent to that of any of the latest unrepeatable studies on power posing and finite will power. 4. Again, it was intentionally misleading (as much as someone wholeheartedly convinced of something is misleading), and doesn't stand up to even contemporary criticisms (IMO). I do think we have objective standards, but I don't think those objective standards are reflected or implied by NHST. Nor do I think a modern person using NHST is somehow a eugenicist by transitivity. It's more that my position is NHST has infected the scientific community precisely because of its self-reported objectivity. I think it's my field's (statistics) personal failure to bear. I think we stayed content too long as the dispensers of "objective methods" without really doing a good job of stressing the nuances and frailties associated with any particular method.