And yet even you who are more against frequentist statistics than most (Given that you are even writing this among other things on the topic) inevitably use the frequentist tools. What I’d be interested in is a good and short(as short as it can be) summary of what methods should be followed to remove as many of the problems of frequentist statistics with properly defined cut-offs for p-values and everything else, where can we fully adapt Bayes, where we can minimize the problems of the frequentist tools and so on. You know, something that I can use on its own to interpret the data if I am to conduct an experiment today the way that currently seems best.
No, I don’t. My self-experiments have long focused on effect sizes (an emphasis which is very easy to do without disruptive changes), and I have been using BEST as a replacement for t-tests for a while, only including an occasional t-test as a safety blanket for my frequentist readers.
If non-NHST frequentism or even full Bayesianism were taught as much as NHST and as well supported by software like R, I don’t think it would be much harder to use.
That’d be essentially Bayesianism with the (uninformative improper) priors (uniform for location parameters and logarithms of scale parameters) swept under the rug, right?
‘Frequentist tools’ are common approximations, loaded with sometimes-applicable interpretations. A Bayesian can use the same approximation, even under the same name, and yet not be diving into Frequentism.
And yet even you who are more against frequentist statistics than most (Given that you are even writing this among other things on the topic) inevitably use the frequentist tools. What I’d be interested in is a good and short(as short as it can be) summary of what methods should be followed to remove as many of the problems of frequentist statistics with properly defined cut-offs for p-values and everything else, where can we fully adapt Bayes, where we can minimize the problems of the frequentist tools and so on. You know, something that I can use on its own to interpret the data if I am to conduct an experiment today the way that currently seems best.
No, I don’t. My self-experiments have long focused on effect sizes (an emphasis which is very easy to do without disruptive changes), and I have been using BEST as a replacement for t-tests for a while, only including an occasional t-test as a safety blanket for my frequentist readers.
If non-NHST frequentism or even full Bayesianism were taught as much as NHST and as well supported by software like R, I don’t think it would be much harder to use.
I can’t find BEST (as a statistical test or similar...) on Google. What test do you refer to?
http://www.indiana.edu/~kruschke/BEST/
That’d be essentially Bayesianism with the (uninformative improper) priors (uniform for location parameters and logarithms of scale parameters) swept under the rug, right?
Not at all (I wrote a post refuting this a couple months ago but can’t link it from my phone)
http://lesswrong.com/lw/f7t/beyond_bayesians_and_frequentists/ I presume.
Thanks!
I really couldn’t presume to say.
‘Frequentist tools’ are common approximations, loaded with sometimes-applicable interpretations. A Bayesian can use the same approximation, even under the same name, and yet not be diving into Frequentism.