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Andyman's avatar

I really want a shirt that says "Bro do you even Bayes?"

Doctor Hammer's avatar

I notice a few things:

1: You seem to assume that you can accurately tell when survey evidence is misleading. Perhaps that falls under the heading of "well-handled" but identifying whether the survey evidence is misleading seems to be terribly difficult. Sure, if you can tell it is misleading based on verifying against evidence you are more certain of that tells you something (ala #4 in your list) but how do you get there? If information has a very high possibility of being misleading, but you can't be sure, probably the answer is to not update on it, no? (I think that is Adam's point, but I wouldn't want to presume.)

2: I think your discussion under-weights the selection effects of Twitter. If your questions are along the lines of "what do my followers and the followers of their retweets think" then you are maybe fine, although you still have all the caveats of your sample being people who respond to surveys, people who check Twitter enough to notice this and respond before the survey closes, etc. If your questions are "What do humans in general in my country think" nope, right out. Even things like "What do humans who care about this topic and are on Twitter think?" won't be well sampled, because the poll still has to reach them for them to answer. Even beyond problems of adjusting for sample bias (like having 75% women, or whatever, that you can't adjust for because you don't have all the demographics) you still run into the "survey at a shopping mall" effect of the people even exposed to the possibility of taking your survey are a fraction of the possible group you actually care about, but your brain wants to lie to you about that and over generalize.

3: That brings up the next problem: you are always trying to persuade someone with evidence, and that someone is yourself. Evidence that could be misleading but you aren't sure of, the non-transparent non-transparencies as David Levy puts it, is the most dangerous. A transparent non-transparency (a known lie) you can work around, but if you are not sure it is a lie the information is extra dangerous because you will always want to interpret it in a way that supports what you want it to. In that case it might be best to assign it a very, very small update amount, and possibly zero, or even slightly negative just to be sure. After all, if the evidence might be misleading, and you don't know exactly how likely that is, there is a good possibility that the proper updating direction is in the opposite direction of the evidence/brain interaction.

Just some thoughts on that. I dabbled in survey design and that kind of work in my graduate student years, and... whew... it wasn't a field I wanted to get much involved in afterwards. The difficulties of getting information that doesn't actively mislead is only surpassed by the difficulty in telling whether it is misleading or not, and whether misleading about magnitude, direction, or both.

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