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    Home/Original/inverse
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    Inverse View

    It is not the case that The probability assigned to a statistical hypothesis should be interpreted epistemically (as strength of belief in the hypothesis).

    ?Set your confidence on the premises below to see your aggregate.

    Reasons For

    2 perspectives
    Reason for 1 of 2
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    • 1.Epistemic probability assignments to hypotheses require a principled method for determining prior probabilities, which Bayesians have failed to uniquely specify.
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    • 2.Without a unique prior, the epistemic interpretation collapses into subjective arbitrariness, undermining the objectivity that scientific inference demands.
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    • 3.Frequentist accounts (Neyman, Pearson) ground inference in long-run error rates without requiring priors, achieving objectivity the epistemic view cannot.
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    Reason for 2 of 2
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    • 1.The argument's premise that hypotheses cannot be treated as repeatable events conflates the hypothesis with the data-generating process it describes.
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    • 2.Frequentists like von Mises assign probabilities to outcomes of repeatable experimental procedures, not to hypotheses directly, making the supporting argument's target a strawman.
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    • 3.Neyman-Pearson theory replaces probability-of-hypothesis with controlled error probabilities, providing a coherent non-epistemic framework the argument fails to refute.
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    Reasons Against

    1 perspective
    Reason against
    ?
    • 1.A statistical hypothesis cannot be seen as a repeatable event.
      ?

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    • 2.A statistical hypothesis cannot be seen as an event that might have some tendency of occurring.
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    • 3.A physical interpretation of probability requires the event to be repeatable or to have some tendency of occurring.
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