Bayesian is cool because you can make arbitrarily complex models, and when you have the parameters estimated it is really easy to calculate all the cool things you want to.
Bayesian is not cool because estimating the parameters takes bloody ages on a supercomputer, unless you spend ages being really careful to specify your model.
Frequentist statistics is cool because it is a massive big bag of tricks to estimate all sorts of stuff, and pretty much all of the tricks are already in R.
Frequentist statistics is not so cool because calculating all the specific things you want to can be a pain in the ass.
Once either Quantum computers kick in or a better algorithm than MCMC for Bayesian is created, Bayesian will win.
There are some philosophical arguments about the objectivity of the prior in Bayesian statistics, but these wash out in a decision theoretic framework because of the subjectivity of the utility function at the other end of the process.
Also, less than 5% of people reporting p-values really know what a p-value is.
Bayesian is not cool because estimating the parameters takes bloody ages on a supercomputer, unless you spend ages being really careful to specify your model.
Frequentist statistics is cool because it is a massive big bag of tricks to estimate all sorts of stuff, and pretty much all of the tricks are already in R.
Frequentist statistics is not so cool because calculating all the specific things you want to can be a pain in the ass.
Once either Quantum computers kick in or a better algorithm than MCMC for Bayesian is created, Bayesian will win.
There are some philosophical arguments about the objectivity of the prior in Bayesian statistics, but these wash out in a decision theoretic framework because of the subjectivity of the utility function at the other end of the process.
Also, less than 5% of people reporting p-values really know what a p-value is.