Abstract
Abstract Background Scientific fraud is an increasingly vexing problem. Many current programs for fraud detection focus on image manipulation, while techniques for detection based on anomalous patterns that may be discoverable in the underlying numerical data get much less attention, even though these techniques are often easy to apply. Methods We applied statistical techniques in considering and and comparing data sets from 10 researchers in one laboratory and three outside investigators to determine whether anomalous patterns in data from a research teaching specialist (RTS) were likely to have occurred by chance. Rightmost digits of values in RTS data sets were not, as expected, uniform. Equal pairs of terminal digits occurred at higher than expected frequency (>10%) and an unexpectedly large number of data triples commonly produced in such research included values near their means as an element. We applied standard statistical tests (chi-square goodness of fit, binomial probabilities) to determine the likelihood of the first two anomalous patterns and developed a new statistical model to test the third. Results Application of the three tests to various data sets reported by RTS resulted in repeated rejection of the hypotheses (often at p-levels well below 0.001) that anomalous patterns in those data may have occurred by chance. Similar application to data sets from other investigators was entirely consistent with chance occurrence. Conclusions This analysis emphasizes the importance of access to raw data that form the bases of publications, reports, and grant applications in order to evaluate the correctness of the conclusions and the importance of applying statistical methods to detect anomalous, especially potentially fabricated, numerical results.
Highlights
During the past decade, retractions of scientific articles have increased more than 10-fold [1]
We applied statistical techniques in considering and and comparing data sets from 10 researchers in one laboratory and three outside investigators to determine whether anomalous patterns in data from a research teaching specialist (RTS) were likely to have occurred by chance
This analysis emphasizes the importance of access to raw data that form the bases of publications, reports, and grant applications in order to evaluate the correctness of the conclusions and the importance of applying statistical methods to detect anomalous, especially potentially fabricated, numerical results
Summary
Retractions of scientific articles have increased more than 10-fold [1]. A straightforward test of significance based on the binomial distribution can be used to test the significance of an unusually high frequency of equal terminal digit pairs, but there is no such standard test to determine the significance of unusually large numbers of triplicate counts containing values near their average Random variation in these triplicate data that are common components of pharmacological, cell biological, and radiobiological experimentation can be analyzed by modeling the triples as sets of three independent, identical Poisson random variables. We ran simulations generating data sets of triples of independent identical Poisson random variables with comparable means, and the distributions of terminal digits in these sets were consistent with the hypothesis of uniformity Based on these considerations, we believe it is reasonable to suppose that the Mosiman technique applies to the various Coulter count data sets under consideration. The occurrences and distributions of terminal doubles in the Coulter counts of other workers are close to the expected 10%
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