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Frequentist and Bayesian statistics are different tools that answer different questions. Arguing whether one is better than the other is like arguing whether on

Frequentist and Bayesian statistics are different tools that answer different questions. Arguing whether one is better than the other is like arguing whether one should drink water or eat bread. I also think that a lot of the criticism against Frequentist inference comes from people who have experienced trouble applying a clean Frequentist solution in a more complex case and thus switched to Bayesian statistics as a replacement that is easier to handle.

a_bonobo 10y ago
To me, the popularity of frequentist approaches in the literature means that I see many misapplications and erroneous applications to get that holy p<0.05. Bayesian thinking is not that popular with these people so it's not 'tainted' yet. It's a bit like the popularity of Excel - we see many people complain about Excel's automated changing of strings to dates, for example. If we'd all switch to R to fix that problem everybody would complain about stringsAsFactors=T instead.
- digitalzombie 10y ago
  > misapplications and erroneous applications to get that holy p<0.05. Or you can just force them to give confidence interval.
Baeocystin 10y ago
Seriously. It's like arguing whether the left foot or the right is more important for walking.
stdbrouw 10y ago
> Frequentist and Bayesian statistics are different tools that answer different questions. Arguing whether one is better than the other is like arguing whether one should drink water or eat bread. Astronomy and astrology also answer different questions, it's just that astrology doesn't answer the questions we're interested in, and it doesn't even do a great job with the questions it does purport to answer. When quantifying uncertainty, the only quantity of interest is the posterior probability, which Bayesian methods provide and frequentist methods don't. Period. The only reason we accept a likelihood instead is because it can provide a reasonable approximation and might be faster or easier to calculate, and the only reason we'd accept a p-value over the full likelihood function is when we need a quick-and-dirty metric of the importance of a variable or an intervention. These are all valid reasons, and heck, few things are more useful than a quick (frequentist) OLS regression, but to then conclude that frequentist and Bayesian methods are entirely equal and complementary is disingenuous. A good blogpost which deconstructs this line of reasoning in more detail: http://www.bayesianphilosophy.com/how-science-can-permanently-fail/ http://www.bayesianphilosophy.com/how-science-can-permanentl...
viraptor 10y ago
> comes from people who have experienced trouble applying a clean Frequentist solution in a more complex case and thus switched to Bayesian statistics as a replacement that is easier to handle. Sounds to me like a good reason and a good outcome. Don't you think?

a_bonobo 10y ago

To me, the popularity of frequentist approaches in the literature means that I see many misapplications and erroneous applications to get that holy p<0.05. Bayesian thinking is not that popular with these people so it's not 'tainted' yet. It's a bit like the popularity of Excel - we see many people complain about Excel's automated changing of strings to dates, for example. If we'd all switch to R to fix that problem everybody would complain about stringsAsFactors=T instead.

digitalzombie 10y ago
> misapplications and erroneous applications to get that holy p<0.05. Or you can just force them to give confidence interval.

Baeocystin 10y ago

Seriously. It's like arguing whether the left foot or the right is more important for walking.

stdbrouw 10y ago

> Frequentist and Bayesian statistics are different tools that answer different questions. Arguing whether one is better than the other is like arguing whether one should drink water or eat bread. Astronomy and astrology also answer different questions, it's just that astrology doesn't answer the questions we're interested in, and it doesn't even do a great job with the questions it does purport to answer. When quantifying uncertainty, the only quantity of interest is the posterior probability, which Bayesian methods provide and frequentist methods don't. Period. The only reason we accept a likelihood instead is because it can provide a reasonable approximation and might be faster or easier to calculate, and the only reason we'd accept a p-value over the full likelihood function is when we need a quick-and-dirty metric of the importance of a variable or an intervention. These are all valid reasons, and heck, few things are more useful than a quick (frequentist) OLS regression, but to then conclude that frequentist and Bayesian methods are entirely equal and complementary is disingenuous. A good blogpost which deconstructs this line of reasoning in more detail: http://www.bayesianphilosophy.com/how-science-can-permanently-fail/ http://www.bayesianphilosophy.com/how-science-can-permanentl...

viraptor 10y ago

> comes from people who have experienced trouble applying a clean Frequentist solution in a more complex case and thus switched to Bayesian statistics as a replacement that is easier to handle. Sounds to me like a good reason and a good outcome. Don't you think?