The Problem Researchers have described challenges and problems in reporting research that uses only p values and power to make decisions to reject the null hypothesis. Confusion about how to interpret null hypothesis statistical tests has resulted from mixed information presented in statistics articles and textbooks. The Solution Combining evidence from data with initial beliefs, Bayesian inference techniques help to provide uncontroversial support of a null hypothesis or alternative hypothesis. An overview of the limitations associated with only using p values and power to make decisions to reject or retain the null hypothesis are presented. Analyses across multiple studies with common parameters can be pooled using Bayesian techniques as a means for conducting meta-analysis. Examples using Bayesian techniques are given. The Stakeholders When designing a research study, researchers often use external elements and likelihood to make powerful inferences using Bayesian techniques. Those performing research in populations requiring sampling.

中文翻译：

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功效分析，p值和贝叶斯技术：如何在HRD文献中使用贝叶斯技术

问题研究人员描述了报告研究中的挑战和问题，这些研究仅使用p值和幂做出决策以拒绝原假设。关于如何解释零假设统计检验的困惑是由于统计文章和教科书中出现的混合信息所致。解决方案贝叶斯推理技术将数据与初始信念的证据相结合，有助于为原假设或替代假设提供无争议的支持。概述了仅使用p值和进行拒绝或保留原假设的决策的局限性。可以使用贝叶斯技术合并具有共同参数的多个研究的分析，作为进行荟萃分析的一种方法。给出了使用贝叶斯技术的例子。利益相关者在设计研究时，研究人员经常使用外部因素和可能性，使用贝叶斯技术做出有力的推论。在需要抽样的人群中进行研究的人员。