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    Blending Bayesian and Classical Tools to Define Optimal Sample-Size-Dependent Significance Levels by Mark Andrew Gannon (6491468)

    Published 2019
    “…A testing procedure is developed using a mixture of frequentist and Bayesian tools, with a significance level that is a function of sample size, obtained from a generalized form of the Neyman–Pearson Lemma that minimizes a linear combination of <i>α</i>, the probability of rejecting a true null hypothesis, and <i>β</i>, the probability of failing to reject a false null, instead of fixing <i>α</i> and minimizing <i>β</i>. …”
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