论文标题
均值最小的差异：效果大小强度和实际意义的统计数据
The Least Difference in Means: A Statistic for Effect Size Strength and Practical Significance
论文作者
论文摘要
由于资源有限，因此必须根据其实际意义进行进一步的研究，资金和翻译来确定科学询问：效果大小是否足够大，可以在现实世界中有意义。这样做必须评估结果的效应强度，这是对实际意义的保守评估。我们提出，平均值（$δ_l$）的最小差异是一个两样本统计量，可以量化效应强度并执行假设检验以确定结果是否具有有意义的效应大小。为了促进共识，$Δ_l$允许科学家比较相关结果之间的效应强度，并选择不同的阈值进行假设检验而无需重新计算。 $δ_l$和相对$Δ_l$均优于其他候选统计数据，以识别具有更高效应强度的结果。我们使用实际数据来说明如何比较广泛相关的实验的效应强度。相对$δ_l$可以根据其结果的强度确定研究优先级。
With limited resources, scientific inquiries must be prioritized for further study, funding, and translation based on their practical significance: whether the effect size is large enough to be meaningful in the real world. Doing so must evaluate a result's effect strength, defined as a conservative assessment of practical significance. We propose the least difference in means ($δ_L$) as a two-sample statistic that can quantify effect strength and perform a hypothesis test to determine if a result has a meaningful effect size. To facilitate consensus, $δ_L$ allows scientists to compare effect strength between related results and choose different thresholds for hypothesis testing without recalculation. Both $δ_L$ and the relative $δ_L$ outperform other candidate statistics in identifying results with higher effect strength. We use real data to demonstrate how the relative $δ_L$ compares effect strength across broadly related experiments. The relative $δ_L$ can prioritize research based on the strength of their results.
