MICRO-COMPUTER PROGRAM FOR STATISTICAL POWER

传统的统计显著性检验侧重于 零假设是可行的。这种方法使研究人员 混淆统计意义和实质性意义，并 错误地将没有显著影响理解为没有 效果是存在的。相比之下，可信区间将注意力集中在 影响的大小，并作为另一个关于精度的问题 这一影响是估计出来的。 我们正在开发一系列计算机程序，使 研究人员在以下框架内规划和解释研究 可信区间。该程序将使研究人员能够报告 一种格式的效果的大小，然后有缺陷 以多种格式显示(赔率比、归因风险、比例 解释的差异等)，这将允许清楚地描述 效果的实质价值。该程序将显示 关于效果的可信区间，以及这些区间如何作为函数变化 样本大小。这将使计划研究的研究人员能够 确保计划的研究的精确度适当 High，研究人员分析了一项已完成的研究，以呈现 结果综合评价。

Traditional tests of statistical significance focus on whether or not the null hypothesis is viable. This approach leads researchers to confuse statistical significance with substantive significance, and to misinterpret the absence of a significant effect as evidence that no effect exists. Confidence intervals by contrast focus attention on the magnitude of the effect, and as a separate issue on the precision with which that effect is estimated. We are developing a series of computer programs that will enable the researcher to plan and interpret studies within the framework of confidence intervals. The program will enable the researcher to report the magnitude of an effect in one format and then have the defect displayed in multiple formats (odds ratio, attributable risk, proportion variance explained, etc), which will allow for a clear picture of the substantive value of the effect. The program will display the confidence intervals about the effect, and how these vary as a function of sample size. This will enable the researcher planning a study to ensure that the precision of the planned study will be appropriately high, and the researcher analyzing a completed study to present the results comprehensively.