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Increasing the specificity of the test lowers the probability of type I errors, but raises the probability of type II errors (false negatives that reject the alternative hypothesis when it is true).
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The latter is known as the false positive risk (see Ambiguity in the definition of false positive rate, below ).
"The reproducibility of research and the misinterpretation of p-values".
It wouldn't even reach the 5 percent level.
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