检验均数差异的t检验的统计学显着性或p值已广泛用于两组的比较。但是，由于统计意义具有许多问题，因此在医学和社会科学领域受到了广泛的批评。因此，已经提出了诸如Cohen d的效应量作为统计显着性的替代方法。最近，已经提出了严格标准化的均值差（SSMD），用于比较两组，并将其应用于高通量筛选实验中的数据分析。在本文中，我将从理论基础上探讨SSMD，标准化均值差和经典t的p值之间的关系。-测试以比较两组。这些措施之间的关系表明，SSMD可以替代医学科学中的统计意义，也可以替代社会科学中传统效应的大小。
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Strictly Standardized Mean Difference, Standardized Mean Difference and Classical t-test for the Comparison of Two Groups
Statistical significance or p-value of t-test for testing mean difference has been widely used for the comparison of two groups. However, because of many issues that the statistical significance has, it has been intensively criticized in medical and social sciences. Consequently, effect sizes such as Cohen’s d have been proposed as an alternative to statistical significance. Recently, strictly standardized mean difference (SSMD) has been proposed for the comparison of two groups with applications in data analysis in high-throughput screening experiments. In this article, from a theoretical basis, I explore the relationships between SSMD, standardized mean difference, and p-value of classical t-test for comparing two groups. The relationships among these measures hint that SSMD may serve as an alternative to statistical significance in medical sciences and as an alternative to traditional effect sizes in social sciences.
