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Comment by ars | Hacker News Reader

Here's the wikipedia page on it: http://en.wikipedia.org/wiki/Gravity_Probe_B

One point about it is that the probe had a lot of noise, so much noise that it swamped the signal. It was only by developing computer models of the source of the noise, and then subtracting it from the signal that the result was found.

But I find such "adjustments" distasteful. I mean everyone expects a certain answer, and voilà you get that answer. Is there a bug in your code? Or course not - you got the expected answer didn't you?

(See the section called "NASA review" in the wikipedia article.)

Anyway, now that they learned so much about such probes they should launch another one and get much cleaner data.

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For those with 65 minutes of time, here's an excellent presentation about it by Dr. Everitt himself:

http://einstein.stanford.edu/Media/Everitt_Brainstorm-flash....

But I find such "adjustments" distasteful.

Well, you have to deal with it somehow. If you know the source of the noise, there's no excuse for not modelling it.

Sure, but the point is that if there's something wrong with your model of the noise, the result could be completely invalid.

Look at the history of Milliken's oil-drop experiment: http://en.wikipedia.org/wiki/Oil_drop_experiment#Millikan.27...

He got the wrong answer, but subsequent researchers who got different results kind of smoothed their own results to match the "correct" (actually wrong) expected result.

I'm not saying that happened here, but it's always a possibility.

Essentially, how do you know when you've modelled the noise accurately enough? When you get the expected right answer? Why not continue to refine the noise model, or consider other ones?

The role that psychology and social dynamics plays in science ought to be more studied. To ignore it is foolish. One person I knew in commutative algebra said that he always adds references to the famous people in the field in his papers for ass kissing purposes. It's just one anecdote but this sort of stuff, I suspect, is much more common than it is thought.

http://en.wikipedia.org/wiki/Philosophy_of_science

I like the definition of humility by Eliezer Yudkowsky on less wrong - "To be humble is to take specific actions in anticipation of your own errors."

Given Dr. Everitt's commitment to this experiment, I'd wager he was humble enough according to that definition ... though that assumption of mine would simply be a "halo effect" instance?

What you are describing is a very real danger, but if you are modeling actual noise, it's less of a risk than if you are modeling some small systematic shift that you claim you must subtract from the signal. With noise, it's more unlikely that the model would just by chance give a systematic shift to the data. More likely, if the model is wrong, it just won't decrease the noise level.

It's was actually systematic shift, but a periodic one. It wasn't random noise.

Well, true. If the noise was truly random, you couldn't model it, of course. I just meant "noise" in the sense of "apparently random variations that actually aren't random".

That's not quite true, since a small signal+random noise will certainly give you a prediction that is testable.

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