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    Frequentist accounts (Neyman, Pearson) ground inference i... — Carmelics
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    Challenges→The probability assigned to a statistical hypothesis should be interpreted epistemically (as strength of belief in the hypothesis).

    Frequentist accounts (Neyman, Pearson) ground inference in long-run error rates without requiring priors, achieving objectivity the epistemic view cannot.

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    Reasons For

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    • 1.Long-run error rates are empirically verifiable properties independent of any analyst's prior beliefs or subjective assumptions.
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    • 2.Frequentist methods guarantee reproducibility: the same data analyzed repeatedly yields identical error-rate calibrations across contexts.
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    • 3.Priors in Bayesian approaches require justification, introducing hidden subjective choices that mask rather than eliminate epistemic commitments.
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    Reasons Against

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    • 1.Long-run frequencies are meaningless for single, unrepeatable experiments (medical diagnosis, climate prediction) where inference is actually needed.
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    • 2.Frequentist inference smuggles in implicit assumptions (stopping rules, sample space definitions) that are equally subjective as explicit priors.
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    • 3.Error-rate guarantees about procedures tell us nothing about the probability that a specific conclusion is true given observed data.
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    Related

    Error-rate guarantees about procedures tell us nothing about the probability tha...Frequentist inference smuggles in implicit assumptions (stopping rules, sample s...Frequentist methods guarantee reproducibility: the same data analyzed repeatedly...Long-run error rates are empirically verifiable properties independent of any an...
    +3 moreShow less
    Long-run frequencies are meaningless for single, unrepeatable experiments (medic...Priors in Bayesian approaches require justification, introducing hidden subjecti...The probability assigned to a statistical hypothesis should be interpreted epist...

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