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.
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.