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> Shy away from authority-based arguments. Saying that such and such is true because such and such individual says so, is counterproductive because the other si

> Shy away from authority-based arguments. Saying that such and such is true because such and such individual says so, is counterproductive because the other side can do the same and the debate will be sterile. You can and should provide references and sources, but for the facts and arguments that they carry, not for their authority. Isn't that contradictory ? At some point, if I provide references and sources, it's ultimately because I trust the authors to be authoritative (because of their expertise, not because of their positions.) Otherwise, any source I provide will be met with "yeah, sure, scientist Y wrote so in journal X, but journal X has been wrong before, so your source is wrong."

mejutoco 5y ago
The way I see it it can be taken as a probability (everything else being equal, it probably is true) but never as logical proof. If one side has probability based on authority, and the other has logical proof, logical proof wins, but otherwise it is an heuristic to not argue about every single detail.
marginalia_nu 5y ago
> Otherwise, any source I provide will be met with "yeah, sure, scientist Y wrote so in journal X, but journal X has been wrong before, so your source is wrong." I think it's easy to gloss over the fact that science exists within a context, and if you aren't a participating part of a scientific field, you probably won't have full access to that context. This is a leading factor behind how in many debates, both sides are waving around scientific articles. This has been especially visible in the last few years with the pandemic and the debate around stuff like restrictions and masks and what have you. It takes years to produce quality science, and here we were months into a pandemic and people kept batting each other over the head with preliminary scientific findings as though Moses himself walked down with them from mount Sinai. The truth was we didn't have all the facts, the scientists for the most part were pretty clear about this, but none of that seemed to matter to the armchair epidemiologists engaged in fierce internet-debate. If p=0.05 is sufficient confidence to publish, one in twenty published findings will be wrong. In one study, that isn't bad, but in tens of thousands, that's a lot of dodgy results. In practice, it's probably far worse than that because of publication bias: Scientists tend to want to break new ground and publish extraordinary findings and maybe won't publish boring stuff that just reconfirms that energy is still conserved or whatever. Many just google up some abstracts off arxiv that seem to support their claims and toss them out like pokemon. That's honestly only marginally better than basing your arguments on proverbs, events from the Iliad, and or bible verses. Science just doesn't work like that. Individual articles are not infallible doctrine. Taken as a whole and in context they do form a pretty compelling argument, but individual articles on their own, even published in major journals, are absolutely fallible.
- Sinidir 5y ago
  >If p=0.05 is sufficient confidence to publish, one in twenty published findings will be wrong This is a wrong understanding of p values. Imagine an urn with only fair 50/50 coins and taking out coins to test them. If you reject the coin being fair at 0.05<p then you will on average have 1/20 statistically significant results. All of your statistically significant results will be wrong. Now imagine an urn that only has biased coins. Every time you reject the null hypothesis you are correct. How many statistically significant results are actually true depend on the metaphorical urn that people draw hypothesis out of. If someone is very good at picking likely hypothesis to pan out their statistically significant results are more likely to be true. Also if a field of study has many bad paths you could go down on then you would expect more statistically significant results to be actually wrong.
  - marginalia_nu 5y ago
    Let's not pretend that the practice of throwing a bunch of models at a dataset in search of statistical significance isn't a thing. P-hacking is a pretty big problem in some fields. That's one of those context-things I mentioned. It's easy to go from a field where you can control variables very well, like physics, and look at a field where the variables are all over the map, e.g. psychology or nutrition; and expect the contexts to be similar because science is science. But it's not. The latter fields have really big problems actually replicating published results, many times the findings just never materialize outside of the initial study. It's not all from overt bad science like p-hacking, effects like publication bias also appears to play a big role.
- Uehreka 5y ago
  Oh I _wish_ more debates had people waving around arxiv pre-prints like that, it would be a huge step up. Most people debating on the internet aren’t using scientific sources at all. Sure, there’s been a lot of back-and-forth in the scientific community about the best way to prevent spread and so on. But most real world covid debates aren’t “we agree cloth/surgical masks are cheap and _might_ prevent spread, so it’s worth giving them a shot, but should we really be pushing people towards N-95s? These two preprints on the topic disagree!” They more often center on easily disprovable things like “will wearing a surgical mask (which surgeons wear for hours a day while performing _surgery_) cause me to suffocate from loss of oxygen? Everything has pros and cons you know!”

mejutoco 5y ago

The way I see it it can be taken as a probability (everything else being equal, it probably is true) but never as logical proof. If one side has probability based on authority, and the other has logical proof, logical proof wins, but otherwise it is an heuristic to not argue about every single detail.

marginalia_nu 5y ago

> Otherwise, any source I provide will be met with "yeah, sure, scientist Y wrote so in journal X, but journal X has been wrong before, so your source is wrong." I think it's easy to gloss over the fact that science exists within a context, and if you aren't a participating part of a scientific field, you probably won't have full access to that context. This is a leading factor behind how in many debates, both sides are waving around scientific articles. This has been especially visible in the last few years with the pandemic and the debate around stuff like restrictions and masks and what have you. It takes years to produce quality science, and here we were months into a pandemic and people kept batting each other over the head with preliminary scientific findings as though Moses himself walked down with them from mount Sinai. The truth was we didn't have all the facts, the scientists for the most part were pretty clear about this, but none of that seemed to matter to the armchair epidemiologists engaged in fierce internet-debate. If p=0.05 is sufficient confidence to publish, one in twenty published findings will be wrong. In one study, that isn't bad, but in tens of thousands, that's a lot of dodgy results. In practice, it's probably far worse than that because of publication bias: Scientists tend to want to break new ground and publish extraordinary findings and maybe won't publish boring stuff that just reconfirms that energy is still conserved or whatever. Many just google up some abstracts off arxiv that seem to support their claims and toss them out like pokemon. That's honestly only marginally better than basing your arguments on proverbs, events from the Iliad, and or bible verses. Science just doesn't work like that. Individual articles are not infallible doctrine. Taken as a whole and in context they do form a pretty compelling argument, but individual articles on their own, even published in major journals, are absolutely fallible.

Sinidir 5y ago
>If p=0.05 is sufficient confidence to publish, one in twenty published findings will be wrong This is a wrong understanding of p values. Imagine an urn with only fair 50/50 coins and taking out coins to test them. If you reject the coin being fair at 0.05<p then you will on average have 1/20 statistically significant results. All of your statistically significant results will be wrong. Now imagine an urn that only has biased coins. Every time you reject the null hypothesis you are correct. How many statistically significant results are actually true depend on the metaphorical urn that people draw hypothesis out of. If someone is very good at picking likely hypothesis to pan out their statistically significant results are more likely to be true. Also if a field of study has many bad paths you could go down on then you would expect more statistically significant results to be actually wrong.
- marginalia_nu 5y ago
  Let's not pretend that the practice of throwing a bunch of models at a dataset in search of statistical significance isn't a thing. P-hacking is a pretty big problem in some fields. That's one of those context-things I mentioned. It's easy to go from a field where you can control variables very well, like physics, and look at a field where the variables are all over the map, e.g. psychology or nutrition; and expect the contexts to be similar because science is science. But it's not. The latter fields have really big problems actually replicating published results, many times the findings just never materialize outside of the initial study. It's not all from overt bad science like p-hacking, effects like publication bias also appears to play a big role.
Uehreka 5y ago
Oh I _wish_ more debates had people waving around arxiv pre-prints like that, it would be a huge step up. Most people debating on the internet aren’t using scientific sources at all. Sure, there’s been a lot of back-and-forth in the scientific community about the best way to prevent spread and so on. But most real world covid debates aren’t “we agree cloth/surgical masks are cheap and _might_ prevent spread, so it’s worth giving them a shot, but should we really be pushing people towards N-95s? These two preprints on the topic disagree!” They more often center on easily disprovable things like “will wearing a surgical mask (which surgeons wear for hours a day while performing _surgery_) cause me to suffocate from loss of oxygen? Everything has pros and cons you know!”