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    Home/Original/inverse
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    It is not the case that A hypothesis passes a severe test only if the data agree with the hypothesis and the probability is very low that the data agree with the alternative 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.Bayesian epistemology (Howson & Urbach) holds that evidential support requires prior probabilities, which severity testing systematically excludes.
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    • 2.A hypothesis with near-zero prior probability can pass Mayo's severity criterion while remaining epistemically unwarranted given background knowledge.
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    • 3.Excluding priors conflates the sampling behavior of a test procedure with the rational degree of belief warranted by evidence.
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    Reason for 2 of 2
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    • 1.Duhem's thesis establishes that any experimental test bears on a conjunction of hypotheses, not a single isolated hypothesis.
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    • 2.Mayo's severity criterion presupposes we can cleanly separate the target hypothesis from auxiliary assumptions when assessing data agreement.
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    • 3.If auxiliary assumptions absorb evidential pressure, a hypothesis may pass severity thresholds without genuine corroboration of the target claim.
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    Reasons Against

    1 perspective
    Reason against
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    • Mayo defines a severe test by two conditions: data agreement with the hypothesis, and low probability that the data agree with the alternative hypothesis.
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