Feb. 28th, 2014 01:35 pm
eve11: (chance)
You'd better not have a C-section because it will cause your baby to have weight problems later in life!

Are you KIDDING me? Apparently a doctor wrote this article. And tries to convince us that "a baby needs to be exposed to bacteria that they might not get if delivered by C-section" as the strongest reason why this association is causal. Scientific News Reporting, can you please stop this nonsensical bullshit and, you know, report on the merits and implications of the actual study instead of this drivel that panders to the lowest common denominator? What the hell kind of editor reads this story and says, "Great! Pass it along!"

I deplore the state of science education, science reporting and general analytical thought in our region if this is its best representative.
eve11: (chance)
There is an interesting debate going on over on Larry Wasserman's blog between the old argument of what is "statistics" and what is "Data science."

I've been tracking the comments and I think the debate just ended with this succinct reply:

Whenever I hear the phrase 'data science', I wonder, is there some other kind?

eve11: (dw_headtardis)


(Not even one statistics class?)

ETA: Oh, hey, what do you know, I can pay $30 to find out whether this research is as full of crap as I think it is. Thanks, Elseveir.
eve11: (Default)
As you may recall, my resolution this year was to finish one book per month and write about it. Well, I finished a book this month. It is a bit of a cheat because it is a short book, and I read it this afternoon. Marshall's Tendencies: What Can Economists Know? by John Sutton, is the book, a short monograph on some common fallacies in macroeconomic modeling. A few notes:

Notes )

Other progress: I am still in the middle of several texts, reading in parallel instead of series, I guess.

Scientific Method in Practice: Still on Chapter 5 (deductive logic). Chapter 6 is "Probability": I think I can probably skip/skim that one being as I've got plenty of experience with probability.

Experimental Design: I've read chapters I-III of R.A. Fisher's Design of Experiments, and a related article: "Misunderstandings between experimentalists and observationalists about causal inference". Imai et al. JRSSA, 2008, vol 171 part 2, pp 481--502. I'm going to have to extend my borrowing time on this book. It's pretty neat to read the words from the horse's mouth, as it were. The lady tasting tea. Darwin's plants. "Student's" t-test. It's quite enlightening to think of the simpler aspects of randomization and the implications of model-based statistical theory on experimental design. On the other hand, the JRSSA article goes into more depth and is providing some good context for the modern repercussions of the book's history lesson.

Incidentally, I learned from Gauch's book that Aristotle originally applied the term "Scientia" to those conclusions that were absolutely provable through uncontested assumptions and logical deduction alone. But this has to be relaxed in the face of the inobservability of truth from the real world without iterations and induction. From reading more about experiments/fixed effects/randomization etc. I see the links between the frequentist "fixed effect" paradigm and this standard of truth. The above books/articles (especially the JRSSA one) are helping me understand that point of view, even if I don't quite believe it yet ;)
eve11: (Default)
Great post by Larry Wasserman:

My sister [ profile] kalypso_01 likes this "Drunk Nate Silver" text best:

At 2:30 AM Drunk Nate Silver sends you a text that says "It's 9:38 am" and that's when you read it!
eve11: (Default)
I have spent the last 12 years becoming well-acquainted with statistics, and yet I am still flummoxed by what others could possibly mean when they say "statistical analysis." It's either a magic box that solves everything, or a divining rod imbued by witchcraft.

Here is an article where it appears to be the second. A long interview with linguist Noam Chomsky on AI and "big data".

Can someone tell me what is this black magic? )
eve11: (Default)
Anything up with that? My co-workers are computer scientists and apparently category theory is useful for such study as related to computer languages like Haskell.

There is a parallel out there between categories and probability distributions.... I wonder if there is an application of 'functors' and isomorphism that would describe sufficiency in terms that computer scientists understand.

This is a note to myself to check this out later. I see for example that categories might need to include some notion of sub-probabilities in order to be "complete" in some sense, and that this means that a category theory relationship to probability is not necessarily one-to-one or whatever. I'm curious though, if there may be a category theoretic basis of applied statistics, that would have different implications of inference than using straight probability theory?

And could this possibly extend to continuous measurement?

Why am I thinking so hard on a Sunday? I have fanfic to write, damn it.
eve11: (Default)
And a quick discussion afterward that I might like to turn into a real and citable argument, but must first get up to speed on current thoughts about this (here, likely, Gelman's blog will prove useful).,0,4672619.story

Thoughts? )


eve11: (Default)

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