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Glad you enjoyed that post so much. It really is a shame that we do such a bad job of teaching students about the inherent subjectivity of descriptive statistic

Glad you enjoyed that post so much. It really is a shame that we do such a bad job of teaching students about the inherent subjectivity of descriptive statistics and let students leave their courses with dangerous ideas about the existence of a Holy Grail statistic that will solve all of their problems.

nkkar 10y ago
Your followup post (http://www.johnmyleswhite.com/notebook/2013/03/22/using-norms-to-understand-linear-regression/ http://www.johnmyleswhite.com/notebook/2013/03/22/using-norm...) is excellent. Thank you!
- johnmyleswhite 10y ago
  Thanks! I really should have finished and written the post about the SVD as well. One of these days...
  - agandy 10y ago
    I'd love to read your post on SVDs once it's written
andy_wrote 10y ago
Thanks for writing it! It's one of my favorite math blog posts floating out there.
dhfhduk 10y ago
I agree with you, although I think the problem is a focus on procedures rather than principles in general. It took me a long time to realize that a principled reason for gaussian parametric distributions is the maximum entropy principle. Prior to that point, it had been presented as essentially arbitrary, even by established professors. There seems to be an assumption that theoretical statistics is "too hard", and as a result there's a middle ground that gets left out. I haven't taught general stats courses in awhile (although I've taught advanced ones), but if I did, might start with Bregman divergences, and work down in the manner of your blog post. I think there's an in-between that gets lost. You can teach principles without deriving long proofs of everything along the way. Students don't get taught the underling principles and philosophies to choose from, and I think this leads to the "holy grail" issues you're referring to. It seems to be changing a bit with new interest in Bayesian methods, but that's just the tip of the iceberg.
ttub 10y ago
The idea of misinterpreting metrics is a very general idea and is not specific to statistics. Humans want to distill vast amounts of information to a more manageable amount, like for example a single number. Equity analysts look at accounting metrics, psychologists look at psychometrics test cores, doctors look at some function of blood pressure, etc etc. Any person with deductive and sceptical mental faculties in place, will recognize that these are all simplifications, and cannot be used to deliver a unified truth. Also, being aware of this has very little to do with being technical or not (for example, plenty of programmers only look at a CPU's clock speed to gauge performance). Anyhow, nice post.

nkkar 10y ago

Your followup post (http://www.johnmyleswhite.com/notebook/2013/03/22/using-norms-to-understand-linear-regression/ http://www.johnmyleswhite.com/notebook/2013/03/22/using-norm...) is excellent. Thank you!

johnmyleswhite 10y ago
Thanks! I really should have finished and written the post about the SVD as well. One of these days...
- agandy 10y ago
  I'd love to read your post on SVDs once it's written

andy_wrote 10y ago

Thanks for writing it! It's one of my favorite math blog posts floating out there.

dhfhduk 10y ago

I agree with you, although I think the problem is a focus on procedures rather than principles in general. It took me a long time to realize that a principled reason for gaussian parametric distributions is the maximum entropy principle. Prior to that point, it had been presented as essentially arbitrary, even by established professors. There seems to be an assumption that theoretical statistics is "too hard", and as a result there's a middle ground that gets left out. I haven't taught general stats courses in awhile (although I've taught advanced ones), but if I did, might start with Bregman divergences, and work down in the manner of your blog post. I think there's an in-between that gets lost. You can teach principles without deriving long proofs of everything along the way. Students don't get taught the underling principles and philosophies to choose from, and I think this leads to the "holy grail" issues you're referring to. It seems to be changing a bit with new interest in Bayesian methods, but that's just the tip of the iceberg.

ttub 10y ago

The idea of misinterpreting metrics is a very general idea and is not specific to statistics. Humans want to distill vast amounts of information to a more manageable amount, like for example a single number. Equity analysts look at accounting metrics, psychologists look at psychometrics test cores, doctors look at some function of blood pressure, etc etc. Any person with deductive and sceptical mental faculties in place, will recognize that these are all simplifications, and cannot be used to deliver a unified truth. Also, being aware of this has very little to do with being technical or not (for example, plenty of programmers only look at a CPU's clock speed to gauge performance). Anyhow, nice post.