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    Type I and Type II errors are mathematically coupled thro... — Carmelics
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    Challenges→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.

    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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    Key Terms

    Coupled (mathematically)(in statistical relationships)
    Connected together so that changing one automatically affects the other—you can't adjust one without impacting the other.
    Harm magnitudes(in ethical decision-making)
    The actual sizes or amounts of damage or negative consequences that could result from a mistake.
    Power (in statistics)(in research and testing)
    The ability of a test to correctly detect when something is true; a higher power means you're less likely to miss the truth.
    Sample size(as used in statistics and scientific reasoning)
    The number of examples or instances you observe when trying to figure out if something is generally true.
    empirical knowledge(Locke's epistemology and ongoing philosophical distinction)

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    Knowledge derived from experience and scientific experiment, as opposed to a priori reasoning.
    knowledge(Distinguished from mere true belief, which may be the product of indoctrination and need not exercise deliberative capacities.)
    Justified true belief — true belief that has been arrived at through the exercise of deliberative capacities, including comparison of and deliberation among alternatives.
    type I error(The strict requirement to avoid type I errors in the corpus-building process is what filters out inconclusive evidence)
    In the context of scientific corpus formation, the error of accepting a claim as scientifically established when it is not — i.e., a false positive inclusion.
    type II error(standards of proof and statistical inference)
    Failing to believe something true

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    The choice of significance threshold (e.g., p<0.05 vs p<0.01) is not determined ...

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    The choice of significance threshold (e.g., p<0.05 vs p<0.01) is not determined ...

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