AI 中文总结
针对社会科学研究中标准假设检验仅能检验单一假设的局限，提出分类检验新框架，可在相关定性类别间分配估计量或宣布结果无定论，还将其应用于媒体实验并提供R包辅助实施。
AI 中文摘要
社会科学家依赖假设检验来支撑研究结论，但标准检验仅用于检验单一假设，而非在对立可能性间作出裁决。我们开发了一种新框架“分类检验”作为替代方案。进行分类检验的研究者不选择单一假设进行检验，而是确定在实质上最具相关性的定性区分（“类别”）；该检验要么将估计量分配到某一类别，且错误控制与传统假设检验类似，要么宣布结果无定论。我们认为，分类检验不仅在目标是在对立可能性间作出裁决时优于现有实践，而且在需要检验单一研究假设时也更优，因为分类检验会使该假设面临被反驳的可能。我们通过将该框架应用于一项知名媒体实验来说明其用法，并提供了一个R包以辅助实施。
英文摘要
Social scientists rely on hypothesis testing to support their research conclusions, but the standard tests are designed for testing one hypothesis rather than adjudicating between rival possibilities. We develop a new framework, "classification testing", as an alternative. Instead of selecting one hypothesis to test, a researcher conducting a classification test decides what qualitative distinctions ("classes") are most substantively relevant; the test either assigns the estimand to a class with error control similar to that of a conventional hypothesis test, or declares the result inconclusive. We argue that classification testing is superior to current practice not just when the objective is to adjudicate between rival possibilities but also when there is one research hypothesis to be tested, because classification testing exposes that hypothesis to refutation. We illustrate the framework by applying it to a well-known media experiment and offer an R package to aid in implementation.
CommentsR package will be released soon
