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> Ignoring your attempt at predictably headed ad hominem ???? ad hominem? Where? I'm saying it's very difficult to quiz about the actual controversy itself, b

• somenameforme 4y ago
  You're definitely correct there on my misreading of the tables. To further clarify, I also decided to see precisely what "points" meant rather than continuing to just skim. They chose to rate true/false answers on a -3 to +3 scale driven by the respondent's certainty. This means one question wrong, out of 34, was able to drive up to a 6 point difference at max certainty. And so, somewhat serendipitously, my point, with some 'modification' remains that opposition in no case was rarely able to explain even a single field-specific question missed, at least not at max certainty. As for the replicability - again, this study arbitrarily removed people fully in line with the consensus. That makes it fairly safe to say that replication is a nonstarter. But I'd also add that another red flag for social science papers is when they collect a large number of variables that end up having no relevance to the published conclusion. Large numbers of variables is a key resource in p-hacking. And this paper was collecting all sorts of data that went completely unused and had nothing whatsoever to do with what they ultimately chose to publish.
  • mlyle 4y ago
    > The effect of opposition on the binarized full set of 34 objective knowledge items variable: > Estimate Std. Error df t value Pr(>|t|) > (Intercept) 25.90592 0.43454 13.11332 59.617 <2e-16 ** > opposition -0.664790.06842 2130.90896 -9.717 <2e-16 ** Looks like each point of opposition was about 2/3rds of one more incorrect true/false question. So the people who were most opposed scored >3 questions worse on a 34 question test, on average. > again, this study arbitrarily removed people fully in line with the consensus. Median filtering and trimming saturated measures is common in research like this-- hopefully designed into the original protocol. I do agree it would be nice to see their preregistration. But, weird things happen at the tails-- truncation effects, etc.
    • somenameforme 4y ago
      Like mentioned, I was more interested in the impact on the subtopic specific results because of what we discussed. Giving a quiz on American history to judge your knowledge of French history is obviously not a reasonable idea, even if the skill-set overlap would probably give at least some weak correlation. In the field-specific binarized case (which removes the -3 to +3 noise), the impact is 0.09 points. So that translates to a fraction of a question difference in results. And while I'm aware trimming extreme outliers, regardless of the side they end up on, I'm unaware of any study entirely removing a segment from their sample, from one side only, which is critical to your entire hypothesis, and doing so without any explanation, let alone justification, whatsoever. One thing I'd observe is that the observed effect is small enough that if this culled group had a knowledge specific score below the mean, then it's likely that their entire conclusion would be invalid. One other issue we have not discussed is the methodology not only resulted in a very non-representative sample, but also was indirectly testing something else. The surveys were done on the internet, and all of the general knowledge questions (besides the covid ones, which were just...) have answers which can be looked up in a matter of seconds.
      • mlyle 4y ago
        > I was more interested in the impact on the subtopic specific results because of what we discussed. I think they're both interesting. Broad overconfidence in performance in basic science combined with low performance is an interesting characteristic of a population. The fact that this is represented in those with contrarian views is interesting. > So that translates to a fraction of a question difference in results. The subscale finding, just shows that this same phenomenon appears to also extend to the subjects they are contrarian about. And it's about the same magnitude of effect, because there's very few questions on the subscale. On the big test, a participant does about 2% worse per unit of disagreement. On the subscale, they do about 1.8% worse per unit of disagreement. The effect appears to have the same magnitude and is statistically significant in both cases. This is an invitation to do studies on specific fields of disagreement with better samples and more questions on the subscale. But this early research casts a wide net across many types of anti-consensus view. Study 5 is a small, possibly flawed step in this direction. I'd be particularly interested in a 2 variable analysis that attempts to model how one's overall objective performance and level of disagreement models objective performance in the specific field where they disagreed. > outliers, ... This is all moving goalposts when we were originally discussing the question set. I've already agreed that sociology and psychology research via Turk is problematic and faces many confounds and selection problems.