[SciPy-User] [OT] Bayesian vs. frequentist

Nathaniel Smith njs@pobox....
Wed Feb 15 03:57:48 CST 2012


I don't understand why this is always framed as a "versus" debate.
"Bayesian methods" are the math for figuring out what to believe;
"frequentist methods" are the math for figuring if you're fooling yourself.
It makes perfect sense for engineers and in house estimates to use the
former and the FDA and scientists the latter. Different methods answer
different questions.

I share everyone's frustration with ignorant people misinterpreting
frequentist results, but contra point 18, I have begun to meet people doing
the same to Bayesian methods, and I the tools are getting more accessible
all the time.
On Feb 14, 2012 7:40 PM, "Sturla Molden" <sturla@molden.no> wrote:

>
> After having worked with applied statistics for ~15 years, I have
> reached this conclusion... ;-)
>
>
> Sturla's 20 propositions on Bayesian vs. classical statistics:
> ==============================================================
>
> 1. For simple data, a figure is sufficient, nobody really cares.
>
> 2. For dummy problems with known facit, Bayesian methods tend to be the
> more accurate.
>
> 3. Bayesian methods include prior knowlege. A horse of 400 g is a priori
> less likely than a horse of 400 kg. Frequentists say this is too
> subjective.
>
> 4. Bayesian methods are easier to interpret. Few understand a
> frequenctist confidence interval, albeit everybody they think they do.
>
> 5. Hypothesis testing: Bayesians answer the question we ask. Freuentists
> don't.
>
> 6. Economists investing their own money are bayesians.
>
> 7. Economists investing your money are frequentists.
>
> 8. For basic medical research, nobody cares.
>
> 9. Drug trials: For getting an FDA application approved, frequentists
> often yield a more 'significant result'.
>
> 10. Drug trials: For in-hose liability estimates, Bayesian methods are
> the safer.
>
> 11. Frequentists can always get more significant results by "sampling
> more data".
>
> 12. Frequentists don't care about stopping rules, even though they should.
>
> 13. Bayesians don't care about stopping rules bacause they don't have to.
>
> 14. "Significant" does not mean "important". Any tiny difference can be
> made statistically significant.
>
> 15. For interpreting clinical lab tests, Bayesian methods prevail, e.g.
> predictive values.
>
> 16. Engineers who know their mathematics use Bayesian methods.
>
> 17. Social scientists who don't know their mathematics are frequentists.
>
> 18. SPSS, Excel, Minitab, and SAS make it easy to be an ignorant
> frequentist.
>
> 19. No tool make it easy to be an ignorant bayesian.
>
> 20. Competent analysts use R, Fortran, Matlab or Python.
>
>
>
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