brian_jaress07 March 2010 07:10:19AM2 points [-]

I'm telling it to give the reader the feeling of what it's like to see a smart person fail at something basic because they fail to cross domains, but when writing I couldn't actually come up with a real example that was simple enough to fit in one paragraph.

I would suggest the example of someone not getting the evil bit joke.

It's good because it works both ways. You only need common sense to understand it, but lay people can be intimidated by the context into not applying common sense, and you'll sometimes see domain experts try to implement essentially the same thing because they turn off common sense while in their domain.

brian_jaress23 February 2010 06:02:49PM3 points [-]

I think that, in this case, the underlying problem was not caused by the way frequentist statistics are commonly taught and practiced by working scientists:

In the present case, the null hypothesis is that the old method and the new method produce data from the same distribution; the authors would like to see data that do not lead to rejection of the null hypothesis.

I'm no statistician, but I'm pretty sure you're not supposed to make your favored hypothesis the null hypothesis. That's a pretty simple rule and I think it's drilled into students and enforced in peer review.

I see that as the underlying problem because it reverses the burden of proof. If they had done it the right way around, six data points would have been not enough to support their method instead of being not enough to reject it. Making your favored hypothesis the null hypothesis can allow you, in the extreme, to rely on a single data point.

brian_jaress21 February 2010 07:01:04PM* 3 points [-]

I too would like to see a good explanation of frequentist techniques, especially one that also explains their relationships (if any) to Bayesian techniques.

Based on the tiny bit I know of both approaches, I think one appealing feature of frequentist techniques (which may or may not make up for their drawbacks) is that your initial assumptions are easier to dislodge the more wrong they are.

It seems to be the other way around with Bayesian techniques because of a stronger built-in assumption that your assumptions are justified. You can immunize yourself against any particular evidence by having a sufficiently wrong prior.

EDIT: Grammar

brian_jaress10 February 2010 09:40:04PM* 0 points [-]

I'd rather give a lot money to GiveWell, earmarked for international charities.

OK, let's do that. You win.

We can probably still use "Save babies on Craigslist" or something similar as the slogan if we make some baby-oriented charity the "poster child."

EDIT: spelling

brian_jaress10 February 2010 09:17:34PM0 points [-]

With staff they hire. Certain kinds of problems are both inevitable and fixable once money is in the pipeline.

When you add that much money, you're giving it to the planners, not the plan. If what they're doing doesn't scale to the money they get (though I think it will) they'll do something else. Treat it like one of those business plan contests. Their success so far shows that they know how to do charity work.

It will also get people to join on Facebook, without which there will be no money for anyone.

But I'm not married to that particular charity. I just think that with so much money waiting to be claimed, we're having a little too much fun seeing who can predict the smallest nitty-gritties the farthest away.

brian_jaress10 February 2010 08:45:47PM0 points [-]

They do separate, regional projects, and that number is what they need to carry out the projects they've already committed to.

If they get on Craigslist and start seeing steady money out of it, they can start a bunch of new projects in new areas.

brian_jaress10 February 2010 02:15:34PM* 7 points [-]

Maybe they're not trying very hard.

I'm actually seriously disappointed in how hard we're trying. I saw the discussion start in the comments of the "shut up and divide" thread. I came here expecting people to be all over it like ants on a picnic. Instead, there actually appears to be more thought going into spinning theories about why it would be hard than plans for doing it, and none of it really compares to all the serious thinking about TDT, MWI, or "Free Will."

Of course it's hard. The point is not that it's easy, but that it's relatively easy considering how much money is involved.

Here's my own halfharted stab:

This meme needs

  1. A specific cause that moves people.
  2. A charity that uses money effectively.
  3. A good slogan.

GiveWell shows four charities with its top rating:

  • Village Reach: Vaccines for babies in Africa
  • Stop TB Partnership (Stop TB): tuberculosis treatments
  • Nurse-Family Partnership: Early Childhood Care (USA)
  • Knowledge is Power Program (KIPP): K-12 Education (USA)

Village Reach is the winner, as far as the cause moving people. Saving babies in Africa trumps treating TB worldwide and educating mothers or children in the US. (Nurse-Family Partnership sends nurses to teach mothers how to be mothers.)

For the slogan, how about: "Save babies on Craigslist."

EDIT: links, spelling

brian_jaress07 February 2010 08:07:14AM4 points [-]

What is it about us, the public, and what is it about conformity itself that causes us all to require it of our neighbors and of our artists and then, with consummate fickleness, to forget those who fall into line and eternally celebrate those who do not?

-- Ben Shahn, "The Shape of Content"

brian_jaress03 February 2010 08:30:25AM5 points [-]

Your friend must be pretty hungry by now.

brian_jaress01 February 2010 08:42:25AM3 points [-]

I'm pretty sure the standard reply is, "Sometimes there is no right answer." These are rules for classifying actions as moral or immoral, not rules that describe the behavior of an always moral actor. If every possible action (including inaction) is immoral, then your actions are immoral.

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