Editor
Jordan Anaya
Just post it: the lesson from two cases of fabricated data detected by statistics alone (2013)
Uri Simonsohn
DOI: 10.1177/0956797613480366 PubMed: 23982243
This article describes two case where fabricated data was suspected based upon the summary statistics alone, and eventually confirmed.
There's an important distinction to make between the technique in this article and granularity testing. Granularity testing seeks to identify errors/fraud in the reporting of the statistics. If the raw data is fabricated but reported correctly granularity testing will not identify a problem. In contrast, the technique of this article assumes the raw data is fabricated but the statistics are reported accurately. However, I suppose the technique can also be used in the case that the statistics are fabricated and the raw data does not exist.
Essentially, the author identified publications where the means or standard deviations showed little variation, and ran simulations to see how unlikely these results are. It takes some effort to run these simulations, and at the end of the day you still will only get a p-value that suggests the data are fabricated.
One thing the author did was once one paper was identified as suspicious, he then checked other papers by the same authors to see if they have the same problem. Anything is possible, and any single publication could by chance have unusual data/statistics. But multiple publications with the same problem is a concerning trend, and it is a good practice to check multiple papers before coming to a conclusion of fraud.
Although I don't know often this technique will be applicable, and it takes some time and expertise to implement, this paper serves as a good paradigm for how to investigate cases of fraud so I am accepting the paper.
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