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Nonsense. Frequentist statistics is (relatively) easy to understand. If people can't be trusted with it, they certainly can't be trusted with Bayesian statistic

Nonsense. Frequentist statistics is (relatively) easy to understand. If people can't be trusted with it, they certainly can't be trusted with Bayesian statistics. This is more of an argument not to teach anyone statistics at all, other than some unspecified elite that the author feels qualified to learn it. By the way, the author has some interesting obsessions: https://twitter.com/FamedCelebrity/status/1555134947849113600 https://twitter.com/FamedCelebrity/status/155513494784911360... https://twitter.com/FamedCelebrity/status/1555162475863183361 https://twitter.com/FamedCelebrity/status/155516247586318336... https://twitter.com/JohnZmirak/status/1554969385860796420?s=20&t=U_yWO3utLkgsPivYuEHc4Q https://twitter.com/JohnZmirak/status/1554969385860796420?s=... (retweet)

uniqueuid 4y ago
Let me introduce you to Gerd Gigerenzer's paper "The Null Ritual", in which he brilliantly shows that almost nobody understands the rituals of frequentist statistics [1]. "How many students and teachers noticed that all of the statements were wrong? As Figure 1 shows, none of the students did. Every student endorsed one or more of the illusions about the meaning of a p-value. One might think that these students lack the right genes for statistical thinking and are stubbornly resistant to education. A glance at the performance of their teachers, however, indicates that wishful thinking might not be entirely their fault. Ninety percent of the professors and lecturers also had illusions, a proportion almost as high as among their students. Most surprisingly, 80% of the statistics teachers shared illusions with their students." [1] http://library.mpib-berlin.mpg.de/ft/gg/GG_Null_2004.pdf http://library.mpib-berlin.mpg.de/ft/gg/GG_Null_2004.pdf
- civilized 4y ago
  I don't disagree that many people struggle to properly interpret frequentist statistics. But adopting a Bayesian perspective isn't a magic talisman. The number of people who are competent Bayesians but don't understand frequentist statistics is zero, and no pedagogical changes will improve that.
  - uniqueuid 4y ago
    I don't agree on this one. The problem with frequentism and the null ritual is that it makes statistics easier (just do this one test, read this one number) and renders some kinds of mistakes somewhat harder (publishing a single false positive). At the same time, it makes some bad mistakes much easier, most notably ignoring power, false positives, the garden of forking paths, type S errors and publication bias. The inherent problem is that the null model is not what people assume it is, and the method (or at least the established canon of approaches) don't make you think about it. If you use bayesian methods, you're pretty much forced to spend more time considering the effect size and credibility of your results, and you're basically required to report them. This means that even non-competent bayesians probably have a better contribution to cumulative science.
    - civilized 4y ago
      It sounds like you read Andrew Gelman's blog. You might be interested to review this post: https://statmodeling.stat.columbia.edu/2012/11/10/16808/ https://statmodeling.stat.columbia.edu/2012/11/10/16808/ Frequentist statistics does not force you to accept any hypothesis test with a result p < 0.05 as definitive proof of something. It does not forbid considering prior probability of a result. It just doesn't formalize the consideration of prior probabilities because it is hard to distill this consideration into a formal recipe. Everyone agrees that Bayes' Rule is valid and important, the question is when and how best to use it. > If you use bayesian methods, you're pretty much forced to spend more time considering the effect size and credibility of your results, and you're basically required to report them. You're not forced to do those things well. Any scientific method can be cargo culted.
      - uniqueuid 4y ago
        Yes I think Gelman is doing great work, as is e.g. McElreath [1]. The problem that I care about is not whether frequentist statistics can be taught and used well. They can, and I try in my teaching to do so. The problem is that empirically, frequentist statistics is a fig leaf for a ton of extremely problematic work. And pushing bayesian thinking is currently our best chance to fix this, because it's easier to do a shift in the mental framework than to fix the perception of an existing framework. [1] https://xcelab.net/rm/statistical-rethinking/ https://xcelab.net/rm/statistical-rethinking/
jgrahamc 4y ago
I don't see the relevance of his personal beliefs or prejudices.
- themenomen 4y ago
  Well, the "article" is mostly an opinion piece, it could as well be a Twitter thread. So, at least, there is some value when evaluating a personal opinion in the context of other opinions expressed publicly by the author. I'm not saying this is the case, but I don't think is invalid either.
  - bart_spoon 4y ago
    Of course it’s invalid. Either there is something about the substance of the article that is debatable or there isn’t. Other opinions are completely irrelevant.
    - themenomen 4y ago
      If I write an article about the benefits of X drug and have expressed publicly by other channels my support for the company that produces such drug, I think is relevant for the discussion of the article I just wrote. That's why I said that may be it is not the case here, as I my self don't see any relationship between the linked tweets and the article. But I don't think is to crazy to think that two related opinions can be analyzed together regardless of the channels of distribution.
      - bart_spoon 4y ago
        If you write an article about the benefits of X drug, you have presumably made arguments that can or can not be supported with available evidence. Your other interactions with the company producing the drug are irrelevant.
    - bigbillheck 4y ago
      That's not very Bayesian of you.
patrick451 4y ago
> By the way, the author has some interesting obsessions: > https://twitter.com/FamedCelebrity/status/155513494784911360 https://twitter.com/FamedCelebrity/status/155513494784911360... > https://twitter.com/FamedCelebrity/status/155516247586318336 https://twitter.com/FamedCelebrity/status/155516247586318336... > https://twitter.com/JohnZmirak/status/1554969385860796420?s= https://twitter.com/JohnZmirak/status/1554969385860796420?s=... (retweet) None of those twitter threads have anything to do with statistics pedagogy at all. What sort of irrelevant, ad hominem argument are trying to make here?
- estomagordo 4y ago
- krageon 4y ago
Banana699 4y ago
The twitter links you cite actually make me respect the author more, it shows for example he's a contrarian in a situation where that would cost him a lot, an attitude that I highly respect. Alas, the article is badly written and unprofessionally laid-out, and, ironically given the title, isn't written with a non-statistician in mind.
- civilized 4y ago
  There's a whole cottage industry of audience-captured contrarians who rely on "contrarian trust" to sell you the opposite of whatever the Establishment is selling. It's not any more thoughtful, it's just the opposite.
  - Banana699 4y ago
    There's a not-so-subtle failure mode where you go from "Some of those against the Establishment are cranks" to "All of those who go against the Establishment are cranks", it feels more clever and satisfying than "I really really like the Establishment and can't tolerate it or its opinons being wrong", but it's indistinguishable in input-output behaviour. If you are talking about his statistics opinions, I don't understand enough statistics to know if it's right or wrong or not even wrong. But his other claims gives him a substantial respect-boost regardless of whether he's right or wrong. I simply like contrarians more.
    - civilized 4y ago
      This is all uninteresting to me because contrarian versus Establishment is a pretty poor indicator for truth. Leaning on that is like the famous drunkard leaning on the lamppost, more for support than illumination. The yellow flag in those tweets is the hyperventilating culture warrior attitude. It speaks poorly of ability to humbly and dispassionately analyze topics, regardless of which side it comes from.
      - Banana699 4y ago
        >contrarian versus Establishment is a pretty poor indicator for truth False. It's a pretty reliable proxy if the Establishment is known or plausibly suspected to benefit from falsehood and\or misrepresent truth. If you have a reliable predictor of falsehood, then something which always opposes it is a reliable predictor of truth. I don't know if the statistics Establishment is one such example (probably not), but the trans-activism and war-media Establishments certainly Are reliable predictors of falsehood, and have been shown to be on multiple occasions. That's why I like the author's opinions on those topics more than on statistics, even if I'm not necessarily certain of their truth. He's going in the rough general direction of truth. >hyperventilating culture warrior attitude. It speaks poorly of ability to humbly and dispassionately analyze topics, regardless of which side it comes from. False Again. Plenty of smart people have this attitude, the notorious example off the top of my head is Nassim Nicholas Taleb, incredibly arrogant and quick to ungraciously fail into combat under criticism - yes - but do you actually want to take him one-on-one in statistics? For a more distant example, see Newton's hilarious pettiness in dealing with rivals. Your heuristic yellow flag would have lead you to dismiss him faster than you can say "Universal Gravitation", and we both know you would be utterly and irredeemably wrong.
tgv 4y ago
> Frequentist statistics is (relatively) easy to understand. Easy to misunderstand. NHST creates false beliefs, and that's even when you do everything correctly. If you accept NHST, you believe in "psi", because Bem has proven it.

uniqueuid 4y ago

Let me introduce you to Gerd Gigerenzer's paper "The Null Ritual", in which he brilliantly shows that almost nobody understands the rituals of frequentist statistics [1]. "How many students and teachers noticed that all of the statements were wrong? As Figure 1 shows, none of the students did. Every student endorsed one or more of the illusions about the meaning of a p-value. One might think that these students lack the right genes for statistical thinking and are stubbornly resistant to education. A glance at the performance of their teachers, however, indicates that wishful thinking might not be entirely their fault. Ninety percent of the professors and lecturers also had illusions, a proportion almost as high as among their students. Most surprisingly, 80% of the statistics teachers shared illusions with their students." [1] http://library.mpib-berlin.mpg.de/ft/gg/GG_Null_2004.pdf http://library.mpib-berlin.mpg.de/ft/gg/GG_Null_2004.pdf

civilized 4y ago
I don't disagree that many people struggle to properly interpret frequentist statistics. But adopting a Bayesian perspective isn't a magic talisman. The number of people who are competent Bayesians but don't understand frequentist statistics is zero, and no pedagogical changes will improve that.
- uniqueuid 4y ago
  I don't agree on this one. The problem with frequentism and the null ritual is that it makes statistics easier (just do this one test, read this one number) and renders some kinds of mistakes somewhat harder (publishing a single false positive). At the same time, it makes some bad mistakes much easier, most notably ignoring power, false positives, the garden of forking paths, type S errors and publication bias. The inherent problem is that the null model is not what people assume it is, and the method (or at least the established canon of approaches) don't make you think about it. If you use bayesian methods, you're pretty much forced to spend more time considering the effect size and credibility of your results, and you're basically required to report them. This means that even non-competent bayesians probably have a better contribution to cumulative science.
  - civilized 4y ago
    It sounds like you read Andrew Gelman's blog. You might be interested to review this post: https://statmodeling.stat.columbia.edu/2012/11/10/16808/ https://statmodeling.stat.columbia.edu/2012/11/10/16808/ Frequentist statistics does not force you to accept any hypothesis test with a result p < 0.05 as definitive proof of something. It does not forbid considering prior probability of a result. It just doesn't formalize the consideration of prior probabilities because it is hard to distill this consideration into a formal recipe. Everyone agrees that Bayes' Rule is valid and important, the question is when and how best to use it. > If you use bayesian methods, you're pretty much forced to spend more time considering the effect size and credibility of your results, and you're basically required to report them. You're not forced to do those things well. Any scientific method can be cargo culted.
    - uniqueuid 4y ago
      Yes I think Gelman is doing great work, as is e.g. McElreath [1]. The problem that I care about is not whether frequentist statistics can be taught and used well. They can, and I try in my teaching to do so. The problem is that empirically, frequentist statistics is a fig leaf for a ton of extremely problematic work. And pushing bayesian thinking is currently our best chance to fix this, because it's easier to do a shift in the mental framework than to fix the perception of an existing framework. [1] https://xcelab.net/rm/statistical-rethinking/ https://xcelab.net/rm/statistical-rethinking/

jgrahamc 4y ago

I don't see the relevance of his personal beliefs or prejudices.

themenomen 4y ago
Well, the "article" is mostly an opinion piece, it could as well be a Twitter thread. So, at least, there is some value when evaluating a personal opinion in the context of other opinions expressed publicly by the author. I'm not saying this is the case, but I don't think is invalid either.
- bart_spoon 4y ago
  Of course it’s invalid. Either there is something about the substance of the article that is debatable or there isn’t. Other opinions are completely irrelevant.
  - themenomen 4y ago
    If I write an article about the benefits of X drug and have expressed publicly by other channels my support for the company that produces such drug, I think is relevant for the discussion of the article I just wrote. That's why I said that may be it is not the case here, as I my self don't see any relationship between the linked tweets and the article. But I don't think is to crazy to think that two related opinions can be analyzed together regardless of the channels of distribution.
    - bart_spoon 4y ago
      If you write an article about the benefits of X drug, you have presumably made arguments that can or can not be supported with available evidence. Your other interactions with the company producing the drug are irrelevant.
  - bigbillheck 4y ago
    That's not very Bayesian of you.

patrick451 4y ago

> By the way, the author has some interesting obsessions: > https://twitter.com/FamedCelebrity/status/155513494784911360 https://twitter.com/FamedCelebrity/status/155513494784911360... > https://twitter.com/FamedCelebrity/status/155516247586318336 https://twitter.com/FamedCelebrity/status/155516247586318336... > https://twitter.com/JohnZmirak/status/1554969385860796420?s= https://twitter.com/JohnZmirak/status/1554969385860796420?s=... (retweet) None of those twitter threads have anything to do with statistics pedagogy at all. What sort of irrelevant, ad hominem argument are trying to make here?

estomagordo 4y ago
krageon 4y ago

Banana699 4y ago

The twitter links you cite actually make me respect the author more, it shows for example he's a contrarian in a situation where that would cost him a lot, an attitude that I highly respect. Alas, the article is badly written and unprofessionally laid-out, and, ironically given the title, isn't written with a non-statistician in mind.

civilized 4y ago
There's a whole cottage industry of audience-captured contrarians who rely on "contrarian trust" to sell you the opposite of whatever the Establishment is selling. It's not any more thoughtful, it's just the opposite.
- Banana699 4y ago
  There's a not-so-subtle failure mode where you go from "Some of those against the Establishment are cranks" to "All of those who go against the Establishment are cranks", it feels more clever and satisfying than "I really really like the Establishment and can't tolerate it or its opinons being wrong", but it's indistinguishable in input-output behaviour. If you are talking about his statistics opinions, I don't understand enough statistics to know if it's right or wrong or not even wrong. But his other claims gives him a substantial respect-boost regardless of whether he's right or wrong. I simply like contrarians more.
  - civilized 4y ago
    This is all uninteresting to me because contrarian versus Establishment is a pretty poor indicator for truth. Leaning on that is like the famous drunkard leaning on the lamppost, more for support than illumination. The yellow flag in those tweets is the hyperventilating culture warrior attitude. It speaks poorly of ability to humbly and dispassionately analyze topics, regardless of which side it comes from.
    - Banana699 4y ago
      >contrarian versus Establishment is a pretty poor indicator for truth False. It's a pretty reliable proxy if the Establishment is known or plausibly suspected to benefit from falsehood and\or misrepresent truth. If you have a reliable predictor of falsehood, then something which always opposes it is a reliable predictor of truth. I don't know if the statistics Establishment is one such example (probably not), but the trans-activism and war-media Establishments certainly Are reliable predictors of falsehood, and have been shown to be on multiple occasions. That's why I like the author's opinions on those topics more than on statistics, even if I'm not necessarily certain of their truth. He's going in the rough general direction of truth. >hyperventilating culture warrior attitude. It speaks poorly of ability to humbly and dispassionately analyze topics, regardless of which side it comes from. False Again. Plenty of smart people have this attitude, the notorious example off the top of my head is Nassim Nicholas Taleb, incredibly arrogant and quick to ungraciously fail into combat under criticism - yes - but do you actually want to take him one-on-one in statistics? For a more distant example, see Newton's hilarious pettiness in dealing with rivals. Your heuristic yellow flag would have lead you to dismiss him faster than you can say "Universal Gravitation", and we both know you would be utterly and irredeemably wrong.

tgv 4y ago

> Frequentist statistics is (relatively) easy to understand. Easy to misunderstand. NHST creates false beliefs, and that's even when you do everything correctly. If you accept NHST, you believe in "psi", because Bem has proven it.