等价(形式语言)
逻辑等价性
计算器
计算机科学
数学
反例
考试(生物学)
牙石(牙科)
人工智能
实验科学
统计假设检验
样品(材料)
大样本
理论计算机科学
作者
Jakub Tomek,Aaron Caldwell,David Eisner
标识
DOI:10.1073/pnas.2611548123
摘要
≥ 0.05 as showing "no effect" or "no difference" remains one of the most widespread and problematic misinterpretations in the scientific literature. A statistically nonsignificant result shows only that the data do not provide strong evidence for a difference. This distinction matters because such findings can arise for two very different reasons: Either there is no meaningful difference, or a meaningful difference is present but cannot be detected reliably because of limited sample size or high variability. We highlight equivalence testing as a practical framework for distinguishing between these possibilities. Using the two one-sided tests (TOST) approach, investigators can formally test whether effects large enough to be scientifically, clinically, or practically meaningful can be ruled out. We explain the logic of TOST, show how it distinguishes practical equivalence from inconclusive evidence, and illustrate its use in paired and unpaired comparisons. To support broader adoption, we also introduce a freely available online calculator that enables researchers to perform common equivalence tests without coding.
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