Two-sample p-values test for statistical significance. Yet p-values cannot determine if a result has a negligible (near-zero) effect size, nor compare evidence for negligibility among independent studies. We propose the most difference in means ({\delta}M) statistic to assess the practical insignificance of results by measuring the evidence for a negligible effect size. Both {\delta}M and the relative form of {\delta}M allow hypothesis testing for negligibility and outperform other candidate statistics in identifying results with stronger evidence of negligible effect. We compile results from broadly related experiments and use the relative {\delta}M to compare practical insignificance across different measurement methods and experiment models. Reporting the relative {\delta}M builds consensus for negligible effect size by making near-zero results more quantitative and publishable.
翻译：具有统计意义的两个模版的P值测试。 然而, p值无法确定结果是否具有可忽略( 近零) 效应大小, 也无法比较独立研究之间可忽略的证据。 我们提出了方法上的最大差异( delta}M) 统计数据, 以便通过测量可忽略效应大小的证据来评估结果的实际价值。 $ delta}M 和 $ delta}M 的相对形式都允许对可忽略性进行假设测试, 并优于其他候选人统计数据, 以识别结果, 并有可忽略效应的更强的证据。 我们汇编了与广泛相关的实验结果, 并使用相对的 ~ delta}M 来比较不同测量方法和实验模型的实际价值。 相对的 ~ delta}M 通过使接近零的结果更具量化和可公布性, 建立可忽略效应大小的共识。
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