Or to be a bit more precise: If you have good enough data to do anything useful with frequentist methods then you may use bayesian reasoning as well. What the judge forbade is using bayes to sound scientific when you can’t back up your priors.
What the judge forbade is using bayes to sound scientific when you can’t back up your priors.
The advantage of Bayesianism is that it is open about the relationship between prior beliefs, evidence, and updated beliefs.
Where there is enough data to use frequentist methods, that doesn’t imply one can produce relevant evidence for a case using those methods. I interpret you as agreeing with this based on your response, but feel free to clarify.
Jurors are not going to be able to tell to what extent frequentist methods produce valid evidence or not. It seems to me that if it is a good idea for judges to forbid using Bayesian reasoning because they can see where priors are arbitrary and are worried the jurors can’t, it is an even better idea for judges to forbid frequentist reasoning that doesn’t have a parallel permitted Bayesian process.
The two methods have similar relevance but differing opacity, and the clearer method is being punished because judges can understand its shortcomings. This leaves juries to deal with only evidence that the judge wasn’t able to understand.
Where there is enough data to use frequentist methods, that doesn’t imply one can produce relevant evidence for a case using those methods. I interpret you as agreeing with this based on your response, but feel free to clarify.
Jurors are not going to be able to tell to what extent frequentist methods produce valid evidence or not. It seems to me that if it is a good idea for judges to forbid using Bayesian reasoning because they can see where priors are arbitrary and are worried the jurors can’t, it is an even better idea for judges to forbid frequentist reasoning that doesn’t have a parallel permitted Bayesian process.
I agree with all of this. What I was trying to say is precisely that this isn’t about Bayes vs Fischer or whoever. Perhaps what I should have said to make that clearer is that the judge in this case did not (just) throw out Bayes, he threw out statistical inference.
What are the options? Frequentist statistics, Bayesian statistics, both, or neither?
How many jurors understand statistical significance is surprise assuming one is wrong?
How many scientists understand the grant renewal case, or differences in differences?
No statistics at all.
Or to be a bit more precise: If you have good enough data to do anything useful with frequentist methods then you may use bayesian reasoning as well. What the judge forbade is using bayes to sound scientific when you can’t back up your priors.
Priors don’t come into it. The expert was presenting likelihood ratios directly (though in an obscure form of words).
+1 for biting the bullet. But...
The advantage of Bayesianism is that it is open about the relationship between prior beliefs, evidence, and updated beliefs.
Where there is enough data to use frequentist methods, that doesn’t imply one can produce relevant evidence for a case using those methods. I interpret you as agreeing with this based on your response, but feel free to clarify.
Jurors are not going to be able to tell to what extent frequentist methods produce valid evidence or not. It seems to me that if it is a good idea for judges to forbid using Bayesian reasoning because they can see where priors are arbitrary and are worried the jurors can’t, it is an even better idea for judges to forbid frequentist reasoning that doesn’t have a parallel permitted Bayesian process.
The two methods have similar relevance but differing opacity, and the clearer method is being punished because judges can understand its shortcomings. This leaves juries to deal with only evidence that the judge wasn’t able to understand.
I agree with all of this. What I was trying to say is precisely that this isn’t about Bayes vs Fischer or whoever. Perhaps what I should have said to make that clearer is that the judge in this case did not (just) throw out Bayes, he threw out statistical inference.