Too Big to Fail: Large Samples and the p-Value Problem
Too Big to Fail: Large Samples and the p-Value Problem
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DOI：
10.1287/isre.2013.0480
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发表时间：
2013-12-01
影响因子：
4.9
通讯作者：
Shmueli, Galit
中科院分区：
文献类型：
--
作者：
Lin, Mingfeng;Lucas, Henry C., Jr.;Shmueli, Galit
互联网为信息系统研究人员提供了进行超大样本研究的机会，通常超过10，000个观察结果。大样本有很多优点，但使用统计推断的研究人员必须意识到与之相关的p值问题。在非常大的样本中，p值很快就会变为零，仅仅依靠p值可能会导致研究人员声称支持没有实际意义的结果。在一项大样本IS研究的调查中，我们发现相当多的论文仅依赖于低p值和回归系数的符号来支持他们的假设。这篇研究评论推荐了研究人员可以采取的一系列措施来缓解大样本中的p值问题，并以eBay上超过30万台相机的销售为例进行了说明。我们相信，解决p值问题将增加大样本IS研究的可信度，并为读者提供更多的见解。
The Internet has provided IS researchers with the opportunity to conduct studies with extremely large samples, frequently well over 10,000 observations. There are many advantages to large samples, but researchers using statistical inference must be aware of the p-value problem associated with them. In very large samples, p-values go quickly to zero, and solely relying on p-values can lead the researcher to claim support for results of no practical significance. In a survey of large sample IS research, we found that a significant number of papers rely on a low p-value and the sign of a regression coefficient alone to support their hypotheses. This research commentary recommends a series of actions the researcher can take to mitigate the p-value problem in large samples and illustrates them with an example of over 300,000 camera sales on eBay. We believe that addressing the p-value problem will increase the credibility of large sample IS research as well as provide more insights for readers.