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

josef.pktd@gmai... josef.pktd@gmai...
Tue Feb 14 14:24:57 CST 2012


On Tue, Feb 14, 2012 at 2:40 PM, Sturla Molden <sturla@molden.no> wrote:
>
> After having worked with applied statistics for ~15 years, I have
> reached this conclusion... ;-)

Do you expect an argument? sounds a bit like http://andrewgelman.com/

Even 20 years ago when I was a Bayesian, I didn't understand what most
of the excitement in the religious differences between Bayesian and
Frequentist fundamentalists was all about. ;)

Josef
If you have a hammer, everything looks like a nail.
If you have a screw driver, everything looks like a screw.
(What's difference in differences, and what's generalized method of
moments. Two highlights from Gelman.)

>
>
> 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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