Bayesian vs Frequentist Debate

It's the bayes vs frequentist war again.

Is this the difference between Bayesian and Frequentist approaches?

There are some cases when the difference between bayesian and frequentist statistics is just a matter of interpretation, but not in this case: the mathematics for a bayesian statistical test are different from the mathematics of a frequentist statistical test and they will produce different results, because the former accounts for prior probabilities and the latter does not. The article talks about frequentist tests, as does almost everyone else who mentions statistical tests without further spe

Are there really people other than philosophers who are actually "Bayesians" or "Frequentists"?Frequentist statistics are just a special case of Bayesian statistics with certain implicit, built-in priors - if you pick these priors using Bayesian statistics you'll get the same answers.The frequentest toolbox is just a collection of these useful special cases of Bayesian statistics with priors that usually make sense in practice. The advantage is this greatly simpl

When are Bayesian methods "clearly worse" than frequentist methods, apart from computationally?

I'm not much of a statistician. Can anybody explain to me why Bayesian vs Frequentist is such a political or religious issue? It seems to me they must either describe the same underlying reality correctly, though differently, or else one is objectively wrong. If they are both correct but different, can we not just characterize the differences and use whichever is most appropriate?It it just a matter of taste with differing opinions on the correctness (or morality?) of the guessing tha

The problems with statistics is that it's complicated, both bayesian and frequentist. Specifically, all statistical methods make assumptions about the data, some of which are quite subtle and take effort to understand. Their intricacy is the reason why so many scientists use them incorrectly. It's much less about whether a method is bayesian or frequentist, but whether the specific assumptions made by a method are suitable for the data. This requires a judgement call. One of the advantages of

It's because practicioners of one says that the other camp is wrong and question each other's methodologies. And in academia, questioning one's methodology is akin to saying one is dumb.To understand both camps I summarize like this.Frequentist statistics has very sound theory but is misapplied by using many heuristics, rule of thumbs and prepared tables. It's very easy to use any method and hack the p-value away to get statistically significant results.Bayesian stat

Bayesians and frequentists should bury the hatchet. They are both useful, and the best tool to use depends on the problem/environment. There's no one-size-fits all in statistics.

This is basically the frequentist vs bayesian debate. Looks like you are firmly in the former camp :)