6 ms·

I don't use Bayes factors in my research (2019)

91 points by jasonhansel 3y ago

Konohamaru 3y ago
Milton Friedman was correct: because the true minimum wage is $0.00 (unemployment), he was correct to compare wage increase to the null hypothesis. The potshot in the opening paragraph ("Milton feels bad about the unemployed but good about his theory.") is simultaneously an appeal to emotion and a presumptuous ad hominem.
- constantcrying 3y ago
  The point isn't that Friedman is right or wrong, but that the statistic model tells him to reject his hypothesis, even though he observed a result consistent with his hypothesis. >is simultaneously an appeal to emotion and a presumptuous ad hominem. I don't see how that is the case.
  - ggm 3y ago
    Because it makes Friedman heartless. He feels bad but he still promulgated theories which wreaked the badness he felt bad about. So it goes to character. If he _really_ felt bad, he'd have done what Norbert Weiner did and move out of the field. He stayed an economist. Not so bad feeling, eh?
    - constantcrying 3y ago
      Predicting a negative effect is extremely common in science effecting humans. No reason to abandon a field. >but he still promulgated theories which wreaked the badness he felt bad about No. His prediction was that increasing the minimum wage leads to increased unemployment. He predicted a negative effect and a negative effect happened. None of this has to do with article, which makes a very simple point about statistics.
      - ggm 3y ago
        > None of this has to do with article, which makes a very simple point about statistics. I am addressing a 'how is this ad hom' question not Friedman's character or the article. I seek to explain what view of him would be a critique of character not substance.
        constantcrying 3y ago
        It isn't an adhom because it is irrelevant.
        ggm 3y ago
        This isn't a very good line of reasoning because it's a non sequiteur. At this point I think you're trolling more than discussing rationally.
        constantcrying 3y ago
        It is an article about a statistical concept.
- travisjungroth 3y ago
  Potshot? It just seems like a joke, but one that puts this character (a nod to Milton Friedman but not like a serious insert) in a positive light. He’s pleased by being correct but sympathetic since he was right about something bad happening to people (unemployment).
  - systemvoltage 3y ago
    If anyone that’s taking potshots, it’s the author.
p-e-w 3y ago
The statistical interpretation of observations is so subtle and complex that it's a good idea to assume that any publication from the empirical sciences is complete garbage, until you know for sure that a qualified statistician has supervised the process. A semester of "introduction to statistical methods" (which is all the background that most scientists have) is NOT enough. Imagine a mathematician writing a paper on a medical topic, making all kinds of claims on how things work in the human body – and then that mathematician justifies their expertise by saying "I did a two-week first aid course once, and also, I was really good at biology in school". This is pretty much how lots of science operates when it comes to interpreting results mathematically.
- blablabla123 3y ago
  On the other hand it seems there's also a lack of testing subjects. It's already frequently pointed out how medical results might not represent everyone. I would also assume that e.g. pharmaceutical certification processes do apply more sophisticated statistics.
  - p-e-w 3y ago
    The ugly truth is that lots of "science" being done today isn't actually science – it's a performance art that superficially imitates certain behaviors that are associated with real science. And how could it be otherwise? There are nearly 10 million scientists in the world right now. And all of them are pushing out papers as fast as humanly possible. There isn't anywhere near enough statistical brainpower available to quality-control all of that. Not to mention that most people with sufficient expertise in statistics have better things to do than micromanaging science grad students who have a hard time comprehending Bayes' theorem.
    - randysalami 3y ago
      Bayes theorem for laymen? I read an AI book and it talked about how you can predict the cause from the effects given certain probabilities.
- 3y ago
KingLancelot 3y ago
[dead]
maxminminmax 3y ago
This is one of the most strawman (to put it mildly) things I have ever read.
- constantcrying 3y ago
  Where is it?
jldugger 3y ago
> Note: By theory I merely mean the rationale for investigating the effect of x on y. A theory can be as simple as “I think people value a mug more once they own it”. Hoo boy, the [2019] is well deserved on this one -- that's a dan arielly reference from before The 2021 Accusation and before the recent NPR story refuting his excuse[1]. [1]: https://www.npr.org/2023/07/27/1190568472/dan-ariely-francesca-gino-harvard-dishonesty-fabricated-data https://www.npr.org/2023/07/27/1190568472/dan-ariely-frances...
- neonate 3y ago
  What is the reference? I don't get it.
  - fsckboy 3y ago
    https://en.wikipedia.org/wiki/Dan_Ariely#Accusations_of_data_fraud_and_academic_misconduct https://en.wikipedia.org/wiki/Dan_Ariely#Accusations_of_data...
  - jldugger 3y ago
    I could swear one of his research projects involved asking people to make a mug and pricing it out after they finished. But I guess it was Kahneman that researched mugs? Whoops
    - zeitlupe 3y ago
      https://en.wikipedia.org/wiki/Endowment_effect https://en.wikipedia.org/wiki/Endowment_effect
edbaskerville 3y ago
I screwed around with trying to compute Bayes factors for models of distributions over set partitions, having been led astray by Bayesian phylogenetic inference methods. It was a waste of time--in practice the epistemology was terrible because the choice of prior distributions had such a huge effect on model comparisons. On top of that, the computations were highly unstable so I had to do a lot of fancy multi-temperature MCMC stuff that never quite worked. Unless your priors are based on actual observations, stick with model selection approaches that are based on measured predictive power, or at least plausible approximations thereof, e.g. Aki Vehtari et al. LOO-CV (approximate leave-one-out cross-validation): https://avehtari.github.io/modelselection/ https://avehtari.github.io/modelselection/ https://mc-stan.org/loo/ https://mc-stan.org/loo/
travisjungroth 3y ago
I’ve been working a lot with Bayes factors lately. I don’t want to sound cultish, but I think part of the issue is this stuff doesn’t work “half way”. As soon as you’re talking about the null hypothesis and Bayes factors, you’re mixing up two schools of thought that don’t play nice. Bayes factors work with comparing models. There is no null model. What, 0% effect? Ok, there was a non-zero effect. That model loses since it put the probability of 0% at 1 and everything else at 0. And if you do anything else, you’re encoding some amount of belief into the model, some judgment you’ve made. So, you need to pick two models and compare them. I’m not saying this is right for science. It’s working well for my purposes. One model meaning “as planned”, one model meaning “not as planned”, use the Bayes factor to decide if things are going as planned. But you do need to be explicit about what models you’re comparing. You have to be able to just put some data in and get a probability back, or it’s not going to work. This is what makes this criticism of Bayes factors so unpersuasive. They’re very easy to calculate, but they’re never calculated here! It’s just the ratio of marginal likelihoods, the probability of the data under the model.
- LudwigNagasena 3y ago
  > Bayes factors work with comparing models. So does the traditional Neyman–Pearson hypothesis testing. > There is no null model. Why can’t there be? > What, 0% effect? Ok, there was a non-zero effect. That model loses since it put the probability of 0% at 1 and everything else at 0%. Well, if your null hypothesis is deterministic and says 0% effect, getting anything other than 0% absolutely will make you reject the null hypothesis. But most of the time hypotheses are not deterministic. Usually you sample random variables. > And if you do anything else, you’re encoding some amount of belief into the model, some judgment you’ve made. Traditional hypothesis testing is a particular case of minimising risk, ie the expected value of your loss given possible models and your decision rule. You don’t assume any belief on the probability of a specific model to be true, thus I think it is incorrect to claim that you encode a belief. You don’t even claim that it can be measured. Of course, that makes it impossible to quantify the risk over all possible models. Thus, you only deal with Type I and Type II errors, which values presumes that the null or alternative hypothesis is correct. If you have a probability measure for models, you can simply average your risks over it and get what is known as Bayes risk. That would be encoding some belief.
kgwgk 3y ago
> wait until you understand Bayes Factors I'm not sure that piece will help people to understand Bayes Factors: https://statmodeling.stat.columbia.edu/2019/09/10/i-hate-bayes-factors-when-theyre-used-for-null-hypothesis-significance-testing/#comment-1119100 https://statmodeling.stat.columbia.edu/2019/09/10/i-hate-bay... > In social science, theory alone will not deliver one [hypothesis to test] I guess it's difficult to test a hypothesis when you don't really have one.
MrManatee 3y ago
If the minimum wage is increased $4, the competing explanations seem to be: 1. Change in unemployment is normally distributed with mean 0% and standard deviation 0.606%. 2. Change in unemployment is uniformly distributed between 1% and 10%. I don't really agree that "(1) vs (2)" is a particularly good formulation of the original question ("Would raising the minimum wage by $4 lead to greater unemployment?"). But if it were, how would the math work out? If we observe that unemployment increases 1%, then yes, that piece of evidence is very slightly in favor of explanation (1). This doesn't feel weird or paradoxical to me. But surely we wouldn't want to decide the matter based just on that one inconclusive data point? Instead we would want to look at another instance of the same situation. If in that case an increase of, say, 6% would (almost) conclusively settle the matter in favor of (2), and an increase of, say, 0.8% would (absolutely) conclusively settle the matter in favor of (1).
fridental 3y ago
So you have just one data point and you want to do statistics about it? No matter what you do, the results won't be useful. In Bayesian approach, you start with some distribution that is a wild guess and doesn't even need to base on any knowledge besides of the basics how money work and that unemployment cannot be 0% or 100%. Each data point will refine your distribution until at some dataset size, it will converge to something estimating the reality. You might want to watch an amazingly helpful introduction by Richard McElreath here https://www.youtube.com/watch?v=guTdrfycW2Q https://www.youtube.com/watch?v=guTdrfycW2Q
civilized 3y ago
Bayesian methods are not easy to use, I agree. But that's because they're trying to answer much more meaningful (but harder) questions than frequentist ones, which researchers should be trying to do. You can't ignore Bayesian epistemology just by not using Bayesian methods. The underlying considerations in the Bayesian framework will inevitably become relevant to how you interpret your data, whether or not you use a formal Bayesian method. The thing is, formally, frequentist methods like Null Hypothesis Significance Testing don't tell you what you really want to know. If you get a significant p-value, that means the data you observed wouldn't often happen by chance (within your model of the null). This doesn't actually tell you if your particular hypothesis should be favored. That requires other considerations, including ones that Simonsohn is negative about in this article. For example, Simonsohn's conclusion says: > To use Bayes factors to test hypotheses: you need to be OK with the following two things: > 1. Accepting the null when “the alternative” you consider, and reject, does not represent the theory of interest. > 2. Rejecting a theory after observing an outcome that the theory predicts. He implies that these should be points against Bayes factors. But #2 is something you actually should do sometimes. Demonstrably. If the data suggests a wildly implausible effect size that doesn't show up consistently in other analyses, that should be a point against your theory and in favor of some more mundane explanation, like noisy data from an underpowered study [1]. Not using Bayesian methods is understandable if you don't feel comfortable with the very heavy demands they can make on your statistics acumen. But if you're, say, a social scientist incentivized to get "sexy" results and you refuse to engage with Bayesian epistemology at all, your career will almost certainly just be a contribution of more noise publications to the replication crisis. [1] http://www.stat.columbia.edu/~gelman/presentations/ziff.pdf http://www.stat.columbia.edu/~gelman/presentations/ziff.pdf
- kkoncevicius 3y ago
  > Bayesian methods are not easy to use, I agree. But that's because they're trying to answer much more meaningful (but harder) questions than frequentist ones I think your view about the difference between Frequentist and Bayesian methods is wrong. There is this rant I like from Larry Wasserman [1] on the subject: My opinions have shifted a bit. [...] Bayes-Frequentist debate still matters. And people — including many statisticians — are still confused about the distinction. I thought the basic Bayes-Frequentist debate was behind us. A year and a half of blogging (as well as reading other blogs) convinced me I was wrong here too. And this still does matter. My emphasis on high-dimensional models is germane, however. In our world of high-dimensional, complex models I can’t see how anyone can interpret the output of a Bayesian analysis in any meaningful way. I wish people were clearer about what Bayes is/is not and what frequentist inference is/is not. Bayes is the analysis of subjective beliefs but provides no frequency guarantees. Frequentist inference is about making procedures that have frequency guarantees but makes no pretense of representing anyone’s beliefs. In the high dimensional world, you have to choose: objective frequency guarantees or subjective beliefs. Choose whichever you prefer, but you can’t have both. I don’t care which one people pick; I just wish they would be clear about what they are giving up when they make their choice. [...] Of course, one can embrace objective Bayesian inference. If this means “Bayesian procedures with good frequentist properties” then I am all for it. But this is just frequentist inference in Bayesian clothing." [1]: https://errorstatistics.com/2013/12/27/deconstructing-larry-wasserman/#comment-18941 https://errorstatistics.com/2013/12/27/deconstructing-larry-...
frankreyes 3y ago
Put the minimum wage at $1,000,000. Then, watch unemployment go up.
tpoacher 3y ago
p-values aren't problematic. How people use them is. Same with bayes factors. I've seen people claim "anything above 3 is significant". Incidentally, the theory behind p-values is actually beautiful, and p-values can generalise really well in theory, but in practice most people don't know this. E.g., did you know that you can have "bayesian" p-values? (in the sense that the p-value can be designed to take priors and other models into account, without violating its definition in any way)

91 points by jasonhansel 3y ago

Milton Friedman was correct: because the true minimum wage is $0.00 (unemployment), he was correct to compare wage increase to the null hypothesis. The potshot in the opening paragraph ("Milton feels bad about the unemployed but good about his theory.") is simultaneously an appeal to emotion and a presumptuous ad hominem.

constantcrying 3y ago
The point isn't that Friedman is right or wrong, but that the statistic model tells him to reject his hypothesis, even though he observed a result consistent with his hypothesis. >is simultaneously an appeal to emotion and a presumptuous ad hominem. I don't see how that is the case.
- ggm 3y ago
  Because it makes Friedman heartless. He feels bad but he still promulgated theories which wreaked the badness he felt bad about. So it goes to character. If he _really_ felt bad, he'd have done what Norbert Weiner did and move out of the field. He stayed an economist. Not so bad feeling, eh?
  - constantcrying 3y ago
    Predicting a negative effect is extremely common in science effecting humans. No reason to abandon a field. >but he still promulgated theories which wreaked the badness he felt bad about No. His prediction was that increasing the minimum wage leads to increased unemployment. He predicted a negative effect and a negative effect happened. None of this has to do with article, which makes a very simple point about statistics.
    - ggm 3y ago
      > None of this has to do with article, which makes a very simple point about statistics. I am addressing a 'how is this ad hom' question not Friedman's character or the article. I seek to explain what view of him would be a critique of character not substance.
      - constantcrying 3y ago
        It isn't an adhom because it is irrelevant.
        ggm 3y ago
        This isn't a very good line of reasoning because it's a non sequiteur. At this point I think you're trolling more than discussing rationally.
        constantcrying 3y ago
        It is an article about a statistical concept.
travisjungroth 3y ago
Potshot? It just seems like a joke, but one that puts this character (a nod to Milton Friedman but not like a serious insert) in a positive light. He’s pleased by being correct but sympathetic since he was right about something bad happening to people (unemployment).
- systemvoltage 3y ago
  If anyone that’s taking potshots, it’s the author.

p-e-w 3y ago

The statistical interpretation of observations is so subtle and complex that it's a good idea to assume that any publication from the empirical sciences is complete garbage, until you know for sure that a qualified statistician has supervised the process. A semester of "introduction to statistical methods" (which is all the background that most scientists have) is NOT enough. Imagine a mathematician writing a paper on a medical topic, making all kinds of claims on how things work in the human body – and then that mathematician justifies their expertise by saying "I did a two-week first aid course once, and also, I was really good at biology in school". This is pretty much how lots of science operates when it comes to interpreting results mathematically.

blablabla123 3y ago
On the other hand it seems there's also a lack of testing subjects. It's already frequently pointed out how medical results might not represent everyone. I would also assume that e.g. pharmaceutical certification processes do apply more sophisticated statistics.
- p-e-w 3y ago
  The ugly truth is that lots of "science" being done today isn't actually science – it's a performance art that superficially imitates certain behaviors that are associated with real science. And how could it be otherwise? There are nearly 10 million scientists in the world right now. And all of them are pushing out papers as fast as humanly possible. There isn't anywhere near enough statistical brainpower available to quality-control all of that. Not to mention that most people with sufficient expertise in statistics have better things to do than micromanaging science grad students who have a hard time comprehending Bayes' theorem.
  - randysalami 3y ago
    Bayes theorem for laymen? I read an AI book and it talked about how you can predict the cause from the effects given certain probabilities.
3y ago

KingLancelot 3y ago

[dead]

maxminminmax 3y ago

This is one of the most strawman (to put it mildly) things I have ever read.

constantcrying 3y ago
Where is it?

jldugger 3y ago

> Note: By theory I merely mean the rationale for investigating the effect of x on y. A theory can be as simple as “I think people value a mug more once they own it”. Hoo boy, the [2019] is well deserved on this one -- that's a dan arielly reference from before The 2021 Accusation and before the recent NPR story refuting his excuse[1]. [1]: https://www.npr.org/2023/07/27/1190568472/dan-ariely-francesca-gino-harvard-dishonesty-fabricated-data https://www.npr.org/2023/07/27/1190568472/dan-ariely-frances...

neonate 3y ago
What is the reference? I don't get it.
- fsckboy 3y ago
  https://en.wikipedia.org/wiki/Dan_Ariely#Accusations_of_data_fraud_and_academic_misconduct https://en.wikipedia.org/wiki/Dan_Ariely#Accusations_of_data...
- jldugger 3y ago
  I could swear one of his research projects involved asking people to make a mug and pricing it out after they finished. But I guess it was Kahneman that researched mugs? Whoops
  - zeitlupe 3y ago
    https://en.wikipedia.org/wiki/Endowment_effect https://en.wikipedia.org/wiki/Endowment_effect

edbaskerville 3y ago

I screwed around with trying to compute Bayes factors for models of distributions over set partitions, having been led astray by Bayesian phylogenetic inference methods. It was a waste of time--in practice the epistemology was terrible because the choice of prior distributions had such a huge effect on model comparisons. On top of that, the computations were highly unstable so I had to do a lot of fancy multi-temperature MCMC stuff that never quite worked. Unless your priors are based on actual observations, stick with model selection approaches that are based on measured predictive power, or at least plausible approximations thereof, e.g. Aki Vehtari et al. LOO-CV (approximate leave-one-out cross-validation): https://avehtari.github.io/modelselection/ https://avehtari.github.io/modelselection/ https://mc-stan.org/loo/ https://mc-stan.org/loo/

travisjungroth 3y ago

I’ve been working a lot with Bayes factors lately. I don’t want to sound cultish, but I think part of the issue is this stuff doesn’t work “half way”. As soon as you’re talking about the null hypothesis and Bayes factors, you’re mixing up two schools of thought that don’t play nice. Bayes factors work with comparing models. There is no null model. What, 0% effect? Ok, there was a non-zero effect. That model loses since it put the probability of 0% at 1 and everything else at 0. And if you do anything else, you’re encoding some amount of belief into the model, some judgment you’ve made. So, you need to pick two models and compare them. I’m not saying this is right for science. It’s working well for my purposes. One model meaning “as planned”, one model meaning “not as planned”, use the Bayes factor to decide if things are going as planned. But you do need to be explicit about what models you’re comparing. You have to be able to just put some data in and get a probability back, or it’s not going to work. This is what makes this criticism of Bayes factors so unpersuasive. They’re very easy to calculate, but they’re never calculated here! It’s just the ratio of marginal likelihoods, the probability of the data under the model.

LudwigNagasena 3y ago
> Bayes factors work with comparing models. So does the traditional Neyman–Pearson hypothesis testing. > There is no null model. Why can’t there be? > What, 0% effect? Ok, there was a non-zero effect. That model loses since it put the probability of 0% at 1 and everything else at 0%. Well, if your null hypothesis is deterministic and says 0% effect, getting anything other than 0% absolutely will make you reject the null hypothesis. But most of the time hypotheses are not deterministic. Usually you sample random variables. > And if you do anything else, you’re encoding some amount of belief into the model, some judgment you’ve made. Traditional hypothesis testing is a particular case of minimising risk, ie the expected value of your loss given possible models and your decision rule. You don’t assume any belief on the probability of a specific model to be true, thus I think it is incorrect to claim that you encode a belief. You don’t even claim that it can be measured. Of course, that makes it impossible to quantify the risk over all possible models. Thus, you only deal with Type I and Type II errors, which values presumes that the null or alternative hypothesis is correct. If you have a probability measure for models, you can simply average your risks over it and get what is known as Bayes risk. That would be encoding some belief.

kgwgk 3y ago

> wait until you understand Bayes Factors I'm not sure that piece will help people to understand Bayes Factors: https://statmodeling.stat.columbia.edu/2019/09/10/i-hate-bayes-factors-when-theyre-used-for-null-hypothesis-significance-testing/#comment-1119100 https://statmodeling.stat.columbia.edu/2019/09/10/i-hate-bay... > In social science, theory alone will not deliver one [hypothesis to test] I guess it's difficult to test a hypothesis when you don't really have one.

MrManatee 3y ago

If the minimum wage is increased $4, the competing explanations seem to be: 1. Change in unemployment is normally distributed with mean 0% and standard deviation 0.606%. 2. Change in unemployment is uniformly distributed between 1% and 10%. I don't really agree that "(1) vs (2)" is a particularly good formulation of the original question ("Would raising the minimum wage by $4 lead to greater unemployment?"). But if it were, how would the math work out? If we observe that unemployment increases 1%, then yes, that piece of evidence is very slightly in favor of explanation (1). This doesn't feel weird or paradoxical to me. But surely we wouldn't want to decide the matter based just on that one inconclusive data point? Instead we would want to look at another instance of the same situation. If in that case an increase of, say, 6% would (almost) conclusively settle the matter in favor of (2), and an increase of, say, 0.8% would (absolutely) conclusively settle the matter in favor of (1).

fridental 3y ago

So you have just one data point and you want to do statistics about it? No matter what you do, the results won't be useful. In Bayesian approach, you start with some distribution that is a wild guess and doesn't even need to base on any knowledge besides of the basics how money work and that unemployment cannot be 0% or 100%. Each data point will refine your distribution until at some dataset size, it will converge to something estimating the reality. You might want to watch an amazingly helpful introduction by Richard McElreath here https://www.youtube.com/watch?v=guTdrfycW2Q https://www.youtube.com/watch?v=guTdrfycW2Q

civilized 3y ago

Bayesian methods are not easy to use, I agree. But that's because they're trying to answer much more meaningful (but harder) questions than frequentist ones, which researchers should be trying to do. You can't ignore Bayesian epistemology just by not using Bayesian methods. The underlying considerations in the Bayesian framework will inevitably become relevant to how you interpret your data, whether or not you use a formal Bayesian method. The thing is, formally, frequentist methods like Null Hypothesis Significance Testing don't tell you what you really want to know. If you get a significant p-value, that means the data you observed wouldn't often happen by chance (within your model of the null). This doesn't actually tell you if your particular hypothesis should be favored. That requires other considerations, including ones that Simonsohn is negative about in this article. For example, Simonsohn's conclusion says: > To use Bayes factors to test hypotheses: you need to be OK with the following two things: > 1. Accepting the null when “the alternative” you consider, and reject, does not represent the theory of interest. > 2. Rejecting a theory after observing an outcome that the theory predicts. He implies that these should be points against Bayes factors. But #2 is something you actually should do sometimes. Demonstrably. If the data suggests a wildly implausible effect size that doesn't show up consistently in other analyses, that should be a point against your theory and in favor of some more mundane explanation, like noisy data from an underpowered study [1]. Not using Bayesian methods is understandable if you don't feel comfortable with the very heavy demands they can make on your statistics acumen. But if you're, say, a social scientist incentivized to get "sexy" results and you refuse to engage with Bayesian epistemology at all, your career will almost certainly just be a contribution of more noise publications to the replication crisis. [1] http://www.stat.columbia.edu/~gelman/presentations/ziff.pdf http://www.stat.columbia.edu/~gelman/presentations/ziff.pdf

kkoncevicius 3y ago
> Bayesian methods are not easy to use, I agree. But that's because they're trying to answer much more meaningful (but harder) questions than frequentist ones I think your view about the difference between Frequentist and Bayesian methods is wrong. There is this rant I like from Larry Wasserman [1] on the subject: My opinions have shifted a bit. [...] Bayes-Frequentist debate still matters. And people — including many statisticians — are still confused about the distinction. I thought the basic Bayes-Frequentist debate was behind us. A year and a half of blogging (as well as reading other blogs) convinced me I was wrong here too. And this still does matter. My emphasis on high-dimensional models is germane, however. In our world of high-dimensional, complex models I can’t see how anyone can interpret the output of a Bayesian analysis in any meaningful way. I wish people were clearer about what Bayes is/is not and what frequentist inference is/is not. Bayes is the analysis of subjective beliefs but provides no frequency guarantees. Frequentist inference is about making procedures that have frequency guarantees but makes no pretense of representing anyone’s beliefs. In the high dimensional world, you have to choose: objective frequency guarantees or subjective beliefs. Choose whichever you prefer, but you can’t have both. I don’t care which one people pick; I just wish they would be clear about what they are giving up when they make their choice. [...] Of course, one can embrace objective Bayesian inference. If this means “Bayesian procedures with good frequentist properties” then I am all for it. But this is just frequentist inference in Bayesian clothing." [1]: https://errorstatistics.com/2013/12/27/deconstructing-larry-wasserman/#comment-18941 https://errorstatistics.com/2013/12/27/deconstructing-larry-...

frankreyes 3y ago

Put the minimum wage at $1,000,000. Then, watch unemployment go up.

tpoacher 3y ago

p-values aren't problematic. How people use them is. Same with bayes factors. I've seen people claim "anything above 3 is significant". Incidentally, the theory behind p-values is actually beautiful, and p-values can generalise really well in theory, but in practice most people don't know this. E.g., did you know that you can have "bayesian" p-values? (in the sense that the p-value can be designed to take priors and other models into account, without violating its definition in any way)