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Agreed. I'm always skeptical of people, like Aella, who focus endlessly on what the data is and trying to interpret grand conclusions from statistics, bigger conclusions than one should. There's a reason "lies, damned lies, and statistics" is a saying.
For example, police statistically pull over and ticket more black people. Does this mean that police are racist? No, it just means that black people commit more traffic offenses. Indeed, black people statistically commit more crime in general. (Noticing this is only racist if you come up with racist explanations for this. There's perfectly innocuous explanations you could argue like black people being historically disadvantaged, being in poverty, etc.) People will argue that speed cameras are better because they can't be biased, and then once speed cameras are implemented, will allege that cameras are racist somehow just because they, statistically, ticket more black people.
Most people don't think in terms of data and statistics, and quite frankly, it's not really the best policy to implement something from "well this number is lower" or "this line is going up and to the right". So what if Meghan Murphy is wrong, and, for the sake of argument, a lot of people in the sex industry have a positive view of it (as proven by statistics)? It does not necessarily follow that the sex industry is ethical or positive for society as a whole.
Just so we're on the same page, there's already articles defending Aella's surveys as things you can draw big conclusions from, rather than things that only apply to Aella's audience.
Definitely. Scott's article is arguing against some hypothetical person that disbelieves Aella's surveys but for some reason believes the average sociological survey, when as far as I'm aware, people who criticize Aella don't also believe in the average survey.
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The whole point of that article, that selection bias is bad, correctly points out that it gives you a conditional expectation when you often want an unconditional one. But then in the very next sentence it says nbd because you often care about correlations, not expectations. Sure, you often care about unconditional correlations, not conditional ones, which is what selection bias gives you.
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Of the two, I’d much rather follow someone who is looking for data, simply because it’s easy to tap dance away from being wrong if you can simply find reasons to not trust the data. It’s perfectly reasonable to propose an alternative theory, or point out an obvious flaw in the data we have. On the other hand, if you’re completely dismissive of the data in hand, you’ve completely lost the ability to think rationally about the issue because you’ve moved from asking whether something is true based on facts to a piori claims that “of course my claim is right, the data you have is flawed, and if we had (what I get to define as) the real data, it would agree with me.”
Online polls of self-selected people have flaws, obviously. But they are at least an attempt at gathering real facts, and they actually do tend to falsify the claim that “women in the sex industry don’t like it” as it shows women in the sex industries liking their job. To simply dismiss that datapoint completely undermines your credibility because it means that your position is not based in fact, but in conjecture. And if you’re basing your opinion on conjecture devoid of facts, it should be dismissed out of hand.
This is my big thing with alien enthusiasts. They are not interested in facts. You point out that we haven’t found any megastructures, they counter with cloaking devices. You tell them that a lot of the the supposed faster than light devices violate known physics or require exotic matter and energy that we can’t find anywhere in the universe, and they point out that the aliens are millions of years ahead of us. And on it goes, dismissing facts at hand as flawed or explaining them away such that the position isn’t based in fact, and it turns out that we have no data at all or the data we have is flawed in such a way that the evidence pointed away from their desired outcome isn’t a problem. It’s dishonest, and I find it much harder to take a position like that seriously if you’re ignoring facts.
What do we do if all the data we have access to really is horribly flawed?
Agnosticism is always an option
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In this case, you point to the flaws in the study and if better are available, cite those. If there’s nothing better, then provisionally accept what we actually have, and go from there. What you don’t get to do is simply say “study bad, therefore it’s all dismissed.” I’m still right because I’m rejecting the data I don’t like, and I reserve the right to reject any data I don’t like on the basis of whether or not I like the studies in question. It’s dishonest in a debate to give yourself the power to simply dismiss evidence without having some data of your own refuting it, Twitter surveys suck as evidence, but absent other evidence from better sources, you can’t simply say “bad methods, so it doesn’t count.” It refuted the point in question, that At least some women enjoy sex work. You can point out that you took a survey of people who follow a prostitute and therefore it’s biased, you can point to a lack of controls to prevent multiple accounts by the same person voting. It’s flawed, but it’s at least some evidence.
But why should I do this if, as posited, I have good reason to think that the data sucks?
Let me give a sort-of example from my own area of expertise. It's not actually a data-driven field; it's very deterministic mathematical theory. For decades now, people have been solving certain problems one way, using one method. The method has significant flaws. Some of the flaws are well-known; others, more damning ones in my mind, are just being revealed now. (I hate to say it, but it truly is, "Being revealed by a series of papers in which I'm a coauthor." I can lessen the arrogant-sounding sting a little bit by wholeheartedly acknowledging that it was a collaborator, not me, who came up with the initial counter-example that kicked off the whole shebang.)
We've been able to fix the problem, using a completely different method (established in a different context)... but so far, only for one specific version. There are numerous other variants of the problem. The thing is, for several of these variants that we've looked at, I can demonstrate that the (very bad) problem exists! I can show actual examples demonstrating why and how the prior methods fail to do what we had previously expected them to do. But we haven't yet 'fixed the glitch' for all these other variants (working on it!).
In sum, I know the bounds of what the prior method actually accomplishes, but I also now know what it doesn't accomplish. This has been hard for some people I've talked to in the field to grok, because they're so steeped in the old method. (I've had this conversation quite a few times, and it really breaks their brains at first, but if I get them to really focus on a particular example and I get them to really consider what would happen with the counterexample, I have a 100% rate of convincing them so far (profs in the field).) If someone were to say something like, "Yeah, ok, well, we know the prior method isn't perfect, but there's nothing better yet for this particular version of the problem, so let's provisionally accept it and go from there," I'm going to say, "HELLS NO!" Instead, I'm likely going to go find a particular counterexample for this variant, show exactly how the existing method is broken for this variant, and simply say, "We can't actually proceed further until we fix this."
I know this is shrouded in a small amount of mystery, but it's related, because we want to say, "Method/data says X." We think that, "Method/data says X." But it turns out that the method/data actually only says Y... which turns out to be very far from actually saying X. I'm not going to provisionally hold X when it pretty clearly says only Y and we don't actually have proper evidence for X.
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