Equivalence margin
The prespecified range of acceptable maximum difference in equivalence testing; if the true effect difference between two methods falls within this range, they are considered practically equivalent.
Timeline (last 90 days)
To prove that two methods are "practically equivalent", an equivalence margin must be pre-specified, and a formal equivalence test must be conducted on that basis.
The equivalence margin must be pre-specified before conducting the equivalence test, and its setting must be based on domain knowledge or practical requirements rather than the data itself, otherwise bias is introduced.
In machine learning, the equivalence margin can be set based on the practical meaning of the metric, for example, an AUROC difference of no more than 0.01.
The study provides a closed-form formula that converts the equivalence margin and target power into the number of runs required for each method, with a shifted-t correction term in the formula calibrated to the actual two-level process.
All Facts (4)
To prove that two methods are "practically equivalent", an equivalence margin must be pre-specified, and a formal equivalence test must be conducted on that basis.
50%UnverifiedThe equivalence margin must be pre-specified before conducting the equivalence test, and its setting must be based on domain knowledge or practical requirements rather than the data itself, otherwise bias is introduced.
50%UnverifiedIn machine learning, the equivalence margin can be set based on the practical meaning of the metric, for example, an AUROC difference of no more than 0.01.
50%UnverifiedThe study provides a closed-form formula that converts the equivalence margin and target power into the number of runs required for each method, with a shifted-t correction term in the formula calibrated to the actual two-level process.
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