4 ms·

Fair point, the beginning section does seem to suggest that. I have a note in the middle explaining this is not the case. I make the connection to demonstrate

Fair point, the beginning section does seem to suggest that. I have a note in the middle explaining this is not the case. I make the connection to demonstrate that Bayes is not some arcane statistical artifact, but qualitatively in line with intuition, albeit miscalibrated. This opens up avenues to then move towards ideal bayesian-ness.

roenxi 6y ago
Ah, hey, didn't see you were also the submitter. I enjoyed your writing; it is interesting to run across systems thinkers. And just to fill up the word count; if you haven't read up on the major statistical paradoxes it is possible you'll enjoy them. https://en.wikipedia.org/wiki/Category:Statistical_paradoxes https://en.wikipedia.org/wiki/Category:Statistical_paradoxes for your attention. If you are playing with stats for the framework then the other half of the fun is delving into the paradoxes; knowing them by heart is a great trick for interpreting evidence. Simpson's, Berkson's and the Elevator Paradox explain a lot of life.
- neilkakkar 6y ago
  Ah, yes indeed! Thank you. I really enjoyed this explanation of Simpson's Paradox: http://michaelnielsen.org/reinventing_explanation/ http://michaelnielsen.org/reinventing_explanation/
  - tmoertel 6y ago
    A better, more satisfying explanation for Simpson's Paradox comes from Pearl et al.'s work in modern causation theory, in which they point out that the paradox exists only because we fail to consider the causal structure that generated the apparently paradoxical data. If we take that structure into account and are clear about the causal question we are trying to answer, the paradox disappears. For a recent example involving COVID-19, check out this blog post: http://causality.cs.ucla.edu/blog/index.php/2020/07/06/race-covid-mortality-and-simpsons-paradox-by-dana-mackenzie/ http://causality.cs.ucla.edu/blog/index.php/2020/07/06/race-...
    - srean 6y ago
      Hi Tom I used to enjoy your blog a lot and your posts on G+ pray continue blogging.

roenxi 6y ago

Ah, hey, didn't see you were also the submitter. I enjoyed your writing; it is interesting to run across systems thinkers. And just to fill up the word count; if you haven't read up on the major statistical paradoxes it is possible you'll enjoy them. https://en.wikipedia.org/wiki/Category:Statistical_paradoxes https://en.wikipedia.org/wiki/Category:Statistical_paradoxes for your attention. If you are playing with stats for the framework then the other half of the fun is delving into the paradoxes; knowing them by heart is a great trick for interpreting evidence. Simpson's, Berkson's and the Elevator Paradox explain a lot of life.

neilkakkar 6y ago
Ah, yes indeed! Thank you. I really enjoyed this explanation of Simpson's Paradox: http://michaelnielsen.org/reinventing_explanation/ http://michaelnielsen.org/reinventing_explanation/
- tmoertel 6y ago
  A better, more satisfying explanation for Simpson's Paradox comes from Pearl et al.'s work in modern causation theory, in which they point out that the paradox exists only because we fail to consider the causal structure that generated the apparently paradoxical data. If we take that structure into account and are clear about the causal question we are trying to answer, the paradox disappears. For a recent example involving COVID-19, check out this blog post: http://causality.cs.ucla.edu/blog/index.php/2020/07/06/race-covid-mortality-and-simpsons-paradox-by-dana-mackenzie/ http://causality.cs.ucla.edu/blog/index.php/2020/07/06/race-...
  - srean 6y ago
    Hi Tom I used to enjoy your blog a lot and your posts on G+ pray continue blogging.