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2 - Scientific Misconduct

Published online by Cambridge University Press:  14 November 2024

Philip Hans Franses
Philip Hans Franses
Affiliation:
Erasmus University
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Summary

In this chapter we discuss a few cases of scientific misconduct that turned out easy to spot, given some basic knowledge of statistics. We learn that it is always important to begin with a close look at the data that you are supposed to analyze. What is the source of the data, how were they collected, and who collected them and for what purpose? Next, we discuss various specific cases where the misconduct was obvious. We see that it is not difficult to create tables with fake regression outcomes, and that it is also not difficult to generate artificial data that match with those tables. Sometimes results are too good to be true. Patterns in outcomes can be unbelievable. We also see that it is not difficult to make the data fit better to a model. These are of course all unethical approaches and should not be replicated, but it is good to know that these things can happen and how.

Keywords

fake dataBenford’s lawvery large t valuesvery small p values
Type
Chapter
Information
Ethics in Econometrics
A Guide to Research Practice
 , pp. 34 - 53
Publisher: Cambridge University Press
Print publication year: 2024

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References

Further Reading

Horton, Joanne, Kumar, Dhanya Krishna, and Wood, Anthony (2020), Detecting academic fraud using Benford law: The case of Professor James Hunton, Research Policy, 49 (8), 104084.CrossRefGoogle Scholar
Harvey, Lee (2020), Research fraud: A long-term problem exacerbated by the glamour for research grants, Quality in Higher Education, 26 (3), 243–261.CrossRefGoogle Scholar
Lacetera, Nicola and Zirulia, Lorenzo (2011), The economics of scientific misconduct, Journal of Law, Economics & Organization, 27 (3), 568–603, with the quote:CrossRefGoogle Scholar
Horbach, Serge P. J. M., Breit, Eric, and Mamelund, Svenn-Erik (2019), Organisational responses to alleged scientific misconduct: Sensemaking, sensegiving, and sensehiding, Science and Public Policy, 46 (3), 415–429.CrossRefGoogle Scholar
Murray, Lori L. and Wilson, John G. (2021), Generating data sets for teaching the importance of regression analysis, Decision Sciences Journal of Innovative Education, 19 (2), 157–166.CrossRefGoogle Scholar
Elliott, Graham, Kudrin, Nikolay, and Wüthrich, Kaspar (2022), Detecting p hacking, Econometrica, 90 (2), 887–906.CrossRefGoogle Scholar
Fang, Ferrie C., Steen, R. Grant, and Casadevall, Arturo (2012), Misconduct accounts for the majority of retracted scientific publications, PNAS, 109 (42), 17028–17033.CrossRefGoogle ScholarPubMed
Brodeur, Abel, Cook, Nikolai, and Neisser, Carina (2022), P-hacking, data type and data-sharing policy, IZA Discussion paper Series No. 15586.Google Scholar

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