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    Actual scientists provisionally abandon theories due to l... — Carmelics
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    Challenges→Learners do not need to have their hypotheses explicitly falsified in the data in order to reject them.

    Actual scientists provisionally abandon theories due to lack of confirmation, make theoretical inferences to link data with theories, employ statistical information, and make defeasible, probabilistic judgments about the truth or falsity of their theories.

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    SkepticismTruth & Knowledge

    Key Terms

    Lack of confirmation(explaining why scientists might stop using a theory)
    When evidence doesn't support or prove that something is true; the absence of proof.
    Probabilistic judgments(describing how scientists evaluate whether theories are correct)
    Decisions or conclusions made based on likelihood or chance rather than certainty—saying something is probably true rather than definitely true.
    Provisionally abandon

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    Related propositions within the same area of thought.
    (describing how scientists treat theories they're not sure about)
    To temporarily set aside or stop using something while leaving open the possibility of returning to it later if new evidence appears.
    Theoretical inferences(describing how scientists connect abstract ideas to actual data)
    Logical conclusions drawn from a theory to explain or predict what we observe in the real world.
    defeasible(Characterizing the logical status of criterial evidence)
    An inference is defeasible when countervailing considerations can weigh against the support a premise provides, potentially nullifying it altogether.

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    Learners do not need to have their hypotheses explicitly falsified in the data i...Learners proceed more like actual scientists do.

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    A scientist can treat a theory as an adequate basis for research witho...83%Moral theories cannot be tested and confirmed in the way that scientif...82%More falsifiable theories are more scientific in the Popperian sense81%Current scientific theories are false.80%

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    Second, let's abandon the idea, which reappears in many presentations of the Argument from the Unlearning Problem; that learners' hypotheses must be explicitly falsified in the data in order to be rejected. Let's suppose instead that learners proceed more like actual scientists do — provisionally abandoning theories due to lack of confirmation, making theoretical inferences to link data with theories, employing statistical information, and making defeasible, probabilistic (rather than decisi

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