Research Commentary - Too Big to Fail: Large Samples and the p-Value Problem

@article{Lin2013ResearchC,
  title={Research Commentary - Too Big to Fail: Large Samples and the p-Value Problem},
  author={Mingfeng Lin and Henry C. Lucas and Galit Shmueli},
  journal={Inf. Syst. Res.},
  year={2013},
  volume={24},
  pages={906-917}
}
The Internet has provided IS researchers with the opportunity to conduct studies with extremely large samples, frequently well over 10,000 observations. [...] Key Result 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.Expand
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References

SHOWING 1-10 OF 68 REFERENCES
An Empirical Analysis of Network Externalities in Peer-to-Peer Music-Sharing Networks
TLDR
The results suggest that the optimal size of these centralized P2P networks is bounded--At some point the costs that a marginal user imposes on the network will exceed the value they provide to the network. Expand
Why We Don't Really Know What Statistical Significance Means: A Major Educational Failure
The Neyman-Pearson theory of hypothesis testing, with the Type I error rate, α, as the significance level, is widely regarded as statistical testing orthodoxy. Fisher’s model of significance testing,Expand
The Sound of Silence in Online Feedback: Estimating Trading Risks in the Presence of Reporting Bias
TLDR
This study offers a method that allows users of feedback mechanisms where both partners of a bilateral exchange are allowed to report their satisfaction to “see through” the distortions introduced by reporting bias and derive unbiased estimates of the underlying distribution of privately observed outcomes. Expand
The Role of Feedback in Managing the Internet-Based Volunteer Work Force
TLDR
In a comparative study of Internet-based voluntary technical support groups for software problems, it is found that in groups who implement systematic quality feedback systems, question askers return over a longer duration, answer providers contribute more often, and technical problem resolution is more effective. Expand
Human Capital and Institutional Effects in the Compensation of Information Technology Professionals in the United States
TLDR
It is found that firms pay a significant premium for an MBA and the IT-related experience of IT professionals and there is no evidence for complementarities between an MBA education and IT experience. Expand
Is the World Really Flat? A Look at Offshoring in an Online Programming Marketplace
TLDR
An online programming marketplace is examined and it is found that this profound tilt to low-wage nations is overstated, and the strongest determinant of the winning bid is client loyalty. Expand
The Significance of Statistical Significance Tests in Marketing Research
Classical statistical significance testing is the primary method by which marketing researchers empirically test hypotheses and draw inferences about theories. The authors discuss the interpretationExpand
The Cult of Statistical Significance
We want to persuade you of one claim: that William Sealy Gosset (1876-1937)—aka "Student" of "Student's" t-test—was right, and that his difficult friend, Ronald A. Fisher (1890-1962), though aExpand
Stata tip 53: Where did my p-values go?
A useful item in the Stata toolkit is the returned result. For example, after most estimation commands, parameter estimates are stored in a matrix e(b). However, these commands do not return the tExpand
The Case Against Statistical Significance Testing
In recent years the use of traditional statistical methods in educational research has increasingly come under attack. In this article, Ronald P. Carver exposes the fantasies often entertained byExpand
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5
...