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

Sturla Molden sturla@molden...
Tue Feb 14 13:40:26 CST 2012

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 

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 

19. No tool make it easy to be an ignorant bayesian.

20. Competent analysts use R, Fortran, Matlab or Python.

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