I guess the natural question is—what about standard Frequentist curriculum? Lots of stuff is neither B or F in stats (for example the book my group and I are going through now).
“it’s still a question of knowing a subject”
Indeed. That’s exactly the point.
The most common way I see “fishing” manifest with Bayesian methods is changing the prior until you get the signal you want. In fact, the “clarity” of Bayesian machinery is even aiding and abetting this type of practice.
You say you are willing to update—don’t you find it weird that basically the only place people still talk about B vs F is here on LW? Professional statisticians moved on from this argument decades ago.
The charitable view is LW likes arguing about unsettled philosophy, but aren’t up to speed on what real philosophical arguments are in the field. (In my field, for example, one argument is about testability, and how much should causal models assume). The uncharitable view is LW is addicted to online wankery.
Let me retrace the steps of this conversation, so that we have at least a direction to move towards. The OP argued that we keep a careful eye so that we don’t drift from Bayesianism as the only correct mathematical form of inference. You try to silence him saying that if he is not a statistician, he should not talk about that. I point out that those who routinely use frequentists statistics are commonly fucking it up (the disaster about the RDA of vitamin D is another easily mockable mistakes of frequentist statisticians). The conversation then degenerates on dick-size measuring, only with IQ or academic credentials.
So, let me regroup what I believe to be true, so that specific parts of what I believe to be true can be attacked (but if it’s just: “you don’t have the credentials to talk about that” or “other intelligent people think differently”, please refrain).
1 the only correct foundation for inference and probability is Bayesian 2 Bayesian probability has a broader applicability than frequentist probability 3 basic frequentist statistics can be and should be reformulated from a Bayesian point of view 4 frequentist statistics is taught badly and applied even worse 5 point 4 bears a no small responsability in famous scientific mistakes 6 nor Bayesian or frequentist statiscs bound dishonest scientists 7 advanced statistics has much more in common with functional analysis and measure theory, so that whether it’s expressed in one or the other form is less important 8 LW has the merit of insisting on Bayes because frequentist statiscs, being the academic tradition, has a higher status, and no amount mistakes derived from it seems able to make a dent in its reputation 9 Bayes theorem is the basis of the first formally defined artificial intelligence
I hope this list can keep the discussion productive.
“The conversation then degenerates on dick-size measuring.”
“I hope this list can keep the discussion productive.”
Alright then, Bayes away!
Generic advice for others: the growth mindset for stats (which is a very hard mathematical subject) is to be more like a grad student, e.g. work very very hard and read a lot, and maybe even try to publish. Leave arguing about philosophy to undergrads.
I guess the natural question is—what about standard Frequentist curriculum? Lots of stuff is neither B or F in stats (for example the book my group and I are going through now).
“it’s still a question of knowing a subject”
Indeed. That’s exactly the point.
The most common way I see “fishing” manifest with Bayesian methods is changing the prior until you get the signal you want. In fact, the “clarity” of Bayesian machinery is even aiding and abetting this type of practice.
You say you are willing to update—don’t you find it weird that basically the only place people still talk about B vs F is here on LW? Professional statisticians moved on from this argument decades ago.
The charitable view is LW likes arguing about unsettled philosophy, but aren’t up to speed on what real philosophical arguments are in the field. (In my field, for example, one argument is about testability, and how much should causal models assume). The uncharitable view is LW is addicted to online wankery.
Let me retrace the steps of this conversation, so that we have at least a direction to move towards.
The OP argued that we keep a careful eye so that we don’t drift from Bayesianism as the only correct mathematical form of inference.
You try to silence him saying that if he is not a statistician, he should not talk about that.
I point out that those who routinely use frequentists statistics are commonly fucking it up (the disaster about the RDA of vitamin D is another easily mockable mistakes of frequentist statisticians).
The conversation then degenerates on dick-size measuring, only with IQ or academic credentials.
So, let me regroup what I believe to be true, so that specific parts of what I believe to be true can be attacked (but if it’s just: “you don’t have the credentials to talk about that” or “other intelligent people think differently”, please refrain).
1 the only correct foundation for inference and probability is Bayesian
2 Bayesian probability has a broader applicability than frequentist probability
3 basic frequentist statistics can be and should be reformulated from a Bayesian point of view
4 frequentist statistics is taught badly and applied even worse
5 point 4 bears a no small responsability in famous scientific mistakes
6 nor Bayesian or frequentist statiscs bound dishonest scientists
7 advanced statistics has much more in common with functional analysis and measure theory, so that whether it’s expressed in one or the other form is less important
8 LW has the merit of insisting on Bayes because frequentist statiscs, being the academic tradition, has a higher status, and no amount mistakes derived from it seems able to make a dent in its reputation
9 Bayes theorem is the basis of the first formally defined artificial intelligence
I hope this list can keep the discussion productive.
“The conversation then degenerates on dick-size measuring.”
“I hope this list can keep the discussion productive.”
Alright then, Bayes away!
Generic advice for others: the growth mindset for stats (which is a very hard mathematical subject) is to be more like a grad student, e.g. work very very hard and read a lot, and maybe even try to publish. Leave arguing about philosophy to undergrads.
This sounds a lot like the Neil Tyson / Bill Nye attitude of “science has made philosophy obsolete!”
I don’t agree with Tyson on this, I just think yall aren’t qualified to do philosophy of stats.