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    Home/Original/inverse
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    Inverse View

    It is not the case that The choice of significance threshold (e.g., p<0.05 vs p<0.01) is not determined by data alone but by judgments about which error type causes more harm.

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    Reasons For

    1 perspective
    Reason for
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    • 1.Type I and Type II errors are mathematically coupled through power and sample size. Choosing thresholds based on consequences requires empirical knowledge of actual harm magnitudes, not just intuition.
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    • 2.If threshold selection depends entirely on subjective judgments of harm, this enables post-hoc rationalization of predetermined conclusions rather than principled decision-making.
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    • 3.Standardized thresholds (p<0.05) enable comparison across studies and guard against cherry-picking thresholds. Contextual variation undermines reproducibility.
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    Reasons Against

    1 perspective
    Reason against
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    • 1.Different domains face asymmetric costs: approving harmful drugs causes deaths; rejecting beneficial ones delays treatment. These aren't mathematically equivalent.
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    • 2.Statistical thresholds are policy choices, not pure data facts. Policy choices legitimately depend on values about acceptable risk, which vary by context.
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    • 3.Pretending thresholds are 'objective' hides value judgments rather than eliminating them, obscuring accountability for consequential decisions.
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