Brent Goldfarb and Andrew King, in a paper to appear in the journal Strategic Management, write:

In a recent issue of this journal, Bettis (2012) reports a conversation with a graduate student who forthrightly announced that he had been trained by faculty to “search for asterisks”. The student explained that he sifted through large databases for statistically significant results and “[w]hen such models were found, he helped his mentors propose theories and hypotheses on the basis of which the ‘asterisks’ might be explained” (p. 109). Such an approach, Bettis notes, is an excellent way to find seemingly meaningful patterns in random data. He expresses concern that these practices are common, but notes that unfortunately “we simply do not have any baseline data on how big or small are the problems” (Bettis, 2012: p. 112).

In this article, we [Goldfarb and King] address the need for empirical evidence . . . in research on strategic management. . . .

Bettis (2012) reports that computer power now allows researchers to sift repeatedly through data in search of patterns. Such specification searches can greatly increase the probability of finding an apparently meaningful relationship in random data. . . . just by trying four functional forms for X, a researcher can increase the chance of a false positive from one in twenty to about one in six. . . .

Simmons et. al (2011) contend that some authors also push almost significant results over thresholds by removing or gathering more data, by dropping experimental conditions, by adding covariates to specified models, and so on.

And, beyond this, there’s the garden of forking paths: even if a researcher performs only *one* analysis of a given dataset, the multiplicity of choices involved in data coding and analysis are such that we can typically assume that different comparisons would have been studied had the data been different. That is, you can have misleading p-values without any cheating or “fishing” or “hacking” going on.

Goldfarb and King continue:

When evidence is uncertain, a single example is often considered representative of the whole (Tversky & Kahneman, 1973). Such inference is incorrect, however, if selection occurs on significant results. In fact, if “significant” results are more likely to be published, coefficient estimates will inflate the true magnitude of the studied effect — particularly if a low powered test has been used (Stanley, 2005).

They conducted a study of “estimates reported in 300 published articles in a random stratified sample from five top outlets for research on strategic management . . . [and] 60 additional proposals submitted to three prestigious strategy conferences.”

And here’s what they find:

We estimate that between 24% and 40% of published findings based on “statistically significant” (i.e. p<0.05) coefficients could not be distinguished from the Null if the tests were repeated once. Our best guess is that for about 70% of non-confirmed results, the coefficient should be interpreted to be zero. For the remaining 30%, the true B is not zero, but insufficient test power prevents an immediate replication of a significant finding. We also calculate that the magnitude of coefficient estimates of most true effects are inflated by 13%.

I’m surprised their estimated exaggeration factor is only 13%; I’d have expected much higher, even if only conditioning on “true” effects (however that is defined).

I have not tried to follow the details of the authors’ data collection and analysis process and thus can neither criticize nor endorse their specific findings. But I’m sympathetic to their general goals and perspective.

As a commenter wrote in an earlier discussion, it is the combination of a strawman with the concept of “statistical significance” (ie the filtering step) that seems to be a problem, not the p-value per se.