原假设显著性检验：两种不同理论的混淆，是广泛混淆和无数误解的基础
Null hypothesis significance tests: A mix-up of two different theories, the basis for widespread confusion and numerous misinterpretations
摘要：
原假设统计显著性检验（NHST）广泛应用于科学计量学等实证科学的定量研究中。然而，自从近一个世纪前引入显著性检验以来，一直存在争议。许多研究人员没有意识到对NHST提出的众多批评。在实践中，NHST被描述为一种无效的仪式，被过度使用，经常被误用和误解。NHST实际上是两个根本不同的经典统计检验模型的拼凑，经常混合一些一厢情愿的准贝叶斯解释。这无疑是NHST经常被误解的一个主要原因。但NHST也有内在的逻辑问题，这些测试提供的信息的认知范围比大多数研究人员认识到的要有限得多。在这篇文章中，我们向科学计量界介绍了NHST的理论起源，这在标准的统计教科书中大多是不存在的，我们讨论了一些最普遍的问题，涉及到NHST的实践，并将这些问题追溯到两个不同的理论起源的混淆。最后，我们用科学计量学文献中的例子说明了一些误解，并提出了一些适度的建议，以便在定量数据分析中进行更合理的实践。
英文摘要：
Null hypothesis statistical significance tests (NHST) are widely used in quantitative research in the empirical sciences including scientometrics. Nevertheless, since their introduction nearly a century ago significance tests have been controversial. Many researchers are not aware of the numerous criticisms raised against NHST. As practiced, NHST has been characterized as a null ritual that is overused and too often misapplied and misinterpreted. NHST is in fact a patchwork of two fundamentally different classical statistical testing models, often blended with some wishful quasi-Bayesian interpretations. This is undoubtedly a major reason why NHST is very often misunderstood. But NHST also has intrinsic logical problems and the epistemic range of the information provided by such tests is much more limited than most researchers recognize. In this article we introduce to the scientometric community the theoretical origins of NHST, which is mostly absent from standard statistical textbooks, and we discuss some of the most prevalent problems relating to the practice of NHST and trace these problems back to the mixup of the two different theoretical origins. Finally, we illustrate some of the misunderstandings with examples from the scientometric literature and bring forward some modest recommendations for a more sound practice in quantitative data analysis.
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