(Forthcoming in Psychology Today) TL;DR key points
2. Assign prior probabilities to the hypotheses 3. Update using Bayes’ theorem 4. Use calibrated probabilities 5. Recognize auxiliary hypotheses 6. Recognize consilience 7. Be cautious about fallible heuristics THE IMPORTANCE OF BAYESIANISM As discussed elsewhere, in many important contexts, we need to form accurate judgments about the world: this is true of medical diagnosis and treatment, of law proceedings, of policy analysis and indeed of a myriad other domains. And as discussed elsewhere, more accurate judgments often means better decisions, including in contexts where they can be a matter of life and death—such as medicine and law. In analytic epistemology and philosophy of science, “Bayesianism” is the dominant theory of how we should form rational judgments of probability. Additionally, as I discuss elsewhere, Bayesian thinking can help us recognize strong evidence and find the truth in cases where others cannot. But there’s ample evidence that humans are not Bayesians, and there’s ample arguments that Bayesians can still end up with inaccurate judgments if they start from the wrong place (i.e. the wrong “priors”). So, given the importance of accurate judgments and given Bayesianism’s potential to facilitate such accuracy, how can one be an accurate Bayesian? Here, I argue that there are seven requirements of highly accurate Bayesians (somewhat carrying on the Steven Coveystyled characterization of rationality which I outlined here). Some requirements will be wellknown to relevant experts (such as requirements 1 to 3) while others might be less so (such as requirements 4 to 7). In any case, this post is written for both the expert and novice, hoping to say something unfamiliar to both—while the familiar remainder can be easily skipped. With that caveat, let us consider the first requirement.
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(Forthcoming in Psychology Today) THE TL;DR KEY POINTS
THE IMPORTANCE OF RECOGNIZING GOOD EVIDENCE We all need to form accurate judgments about the world in many diverse and important contexts. What is the correct diagnosis for someone’s medical condition? Does someone have a crush on you? Did the defendant kill the victim? Here, I will discuss how the evidence can reveal the truth about these questionsand potentially others which you might care aboutbut only if we think in the right ways. It’s welldocumented that various biases can hinder us in our quest for truth. In a recently published paper in Judgment and Decision Making (freely available here), I introduce a new cognitive bias: likelihood neglect bias. Understanding this bias, and how to overcome it, can help us recognize good evidence and find the truth in numerous cases where others might not. To show this, though, I’ll use a wellknown brainteaser which reveals this bias—the Monty Hall problem—and then I’ll apply the emerging ideas to show how we can find the truth in other realistic cases—including medicine, law and more mundane topics. You might then want to apply these ideas to other cases which you might care about. 
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