As the article reports, "Perhaps the worst fallacy is the kind of self-deception for which psychologist Uri Simonsohn of the University of Pennsylvania and his

As the article reports, "Perhaps the worst fallacy is the kind of self-deception for which psychologist Uri Simonsohn of the University of Pennsylvania and his colleagues have popularized the term P-hacking; it is also known as data-dredging, snooping, fishing, significance-chasing and double-dipping. 'P-hacking,' says Simonsohn, 'is trying multiple things until you get the desired result' — even unconsciously." Simonsohn, has a whole website about "p-hacking" and how to detect it. http://www.p-curve.com/ http://www.p-curve.com/ He and his colleagues are concerned about making scientific papers more reliable. You can use the p-curve software on that site for your own investigations into p values found in published research. Many of the interesting issues brought up by the comments on the article kindly submitted here become much more clear after reading Simonsohn's various articles http://opim.wharton.upenn.edu/~uws/ http://opim.wharton.upenn.edu/~uws/ about p values and what they mean, and other aspects of interpreting published scientific research. He also has a paper http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2259879 http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2259879 on evaluating replication results with more specific tips on that issue. "Abstract: "When does a replication attempt fail? The most common standard is: when it obtains p>.05. I begin here by evaluating this standard in the context of three published replication attempts, involving investigations of the embodiment of morality, the endowment effect, and weather effects on life satisfaction, concluding the standard has unacceptable problems. I then describe similarly unacceptable problems associated with standards that rely on effect-size comparisons between original and replication results. Finally, I propose a new standard: Replication attempts fail when their results indicate that the effect, if it exists at all, is too small to have been detected by the original study. This new standard (1) circumvents the problems associated with existing standards, (2) arrives at intuitively compelling interpretations of existing replication results, and (3) suggests a simple sample size requirement for replication attempts: 2.5 times the original sample."