The Problem of New Evidence: P-Hacking and Pre-Analysis Plans
Article Sidebar
Main Article Content
Abstract
We provide a novel articulation of the epistemic peril of p-hacking using three resources from philosophy: predictivism, Bayesian confirmation theory, and model selection theory. We defend a nuanced position on p-hacking: p-hacking is sometimes, but not always, epistemically pernicious. Our argument requires a novel understanding of Bayesianism, since a standard criticism of Bayesian confirmation theory is that it cannot represent the influence of biased methods. We then turn to pre-analysis plans, a methodological device used to mitigate p-hacking. Some say that pre-analysis plans are epistemically meritorious while others deny this, and in practice pre-analysis plans are often violated. We resolve this debate with a modest defence of pre-analysis plans. Further, we argue that pre-analysis plans can be epistemically relevant even if the plan is not strictly followed—and suggest that allowing for flexible pre-analysis plans may be the best available policy option.
Downloads
Article Details
By submitting his/her work to the Editorial Board, the author accepts, upon having his/her text recommended for publication, that Diametros applies the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license to works we publish. Under this license, authors agree to make articles legally available for reuse, without permission or fees, for any purpose except commercial. Anyone may read, download, copy, print, distribute or reuse these articles without asking prior permission from the publisher or the author, as long as the author and original source are properly cited. The author holds the copyright without any other restrictions. Full information about CC-BY-NC: https://creativecommons.org/licenses/by-nc/4.0/legalcode.
References
Backhouse R.E., Morgan M.S. (2000), “Introduction: is Data Mining a Methodological Problem?,” Journal of Economic Methodology 7 (2): 171–181.
View in Google Scholar
DOI: https://doi.org/10.1080/13501780050045065
Barnes E.C. (2008), The Paradox of Predictivism, Cambridge University Press, Cambridge.
View in Google Scholar
DOI: https://doi.org/10.1017/CBO9780511487330
Bright L.K. (2017), “On Fraud,” Philosophical Studies 174 (2): 291–310.
View in Google Scholar
DOI: https://doi.org/10.1007/s11098-016-0682-7
Brodeur A., Lé M., Sangnier M. et al. (2016), “Star Wars: The Empirics Strike Back,” American Economic Journal: Applied Economics 8 (1): 1–32.
View in Google Scholar
DOI: https://doi.org/10.1257/app.20150044
Camerer C.F., Dreber A., Holzmeister F. et al. (2018), “Evaluating the Replicability of Social Science Experiments in Nature and Science Between 2010 and 2015,” Nature Human Behaviour 2 (9): 637–644.
View in Google Scholar
DOI: https://doi.org/10.1038/s41562-018-0399-z
Casey K., Glennerster R., Miguel E. (2012), “Reshaping Institutions: Evidence on Aid Impacts Using a Preanalysis Plan,” The Quarterly Journal of Economics 127 (4): 1755–1812.
View in Google Scholar
DOI: https://doi.org/10.1093/qje/qje027
Chambers C.D. (2013), “Registered Reports: A New Publishing Initiative at Cortex,” Cortex 49 (3): 609–610.
View in Google Scholar
DOI: https://doi.org/10.1016/j.cortex.2012.12.016
Chambers C.D., Feredoes E., Muthukumaraswamy S.D. et al. (2014), “Instead of ‘Playing the Game’ it is Time to Change the Rules: Registered Reports at AIMS Neuroscience and Beyond,” AIMS Neuroscience 1 (1): 4–17.
View in Google Scholar
DOI: https://doi.org/10.3934/Neuroscience.2014.1.4
Christensen G., Miguel E. (2018), “Transparency, Reproducibility, and the Credibility of Economics Research,” Journal of Economic Literature 56 (3): 920–980.
View in Google Scholar
DOI: https://doi.org/10.1257/jel.20171350
Coffman L.C., Niederle M. (2015), “Pre-Analysis Plans have Limited Upside, Especially where Replications are Feasible,” Journal of Economic Perspectives 29 (3): 81–98.
View in Google Scholar
DOI: https://doi.org/10.1257/jep.29.3.81
Diaconis P., Mosteller F. (1989), “Methods for Studying Coincidences,” Journal of the American Statistical Association 84 (408): 853–861.
View in Google Scholar
DOI: https://doi.org/10.1080/01621459.1989.10478847
Douglas H., Magnus P.D. (2013), “State of the Field: Why Novel Prediction Matters,” Studies in History and Philosophy of Science Part A 44 (4): 580–589.
View in Google Scholar
DOI: https://doi.org/10.1016/j.shpsa.2013.04.001
Dwan K., Altman D., Arnaiz J. et al. (2008), “Systematic Review of the Empirical Evidence of Study Publication Bias and Outcome Reporting Bias,” PLoS ONE 3 (8): e3081.
View in Google Scholar
DOI: https://doi.org/10.1371/journal.pone.0003081
FDA (1997), “Food and Drug Administration Modernization Act,” 105th U.S. Congress, U.S. House of Representative Bill, URL = https://www.congress.gov/bill/105th-congress/senate-bill/830 [Accessed 13.07.2020].
View in Google Scholar
Findley M.G., Jensen N.M., Malesky E.J. et al. (2016), “Can Results-Free Review Reduce Publication Bias? The Results and Implications of a Pilot Study,” Comparative Political Studies 49 (13): 1667–1703.
View in Google Scholar
DOI: https://doi.org/10.1177/0010414016655539
Foster A., Karlan D., Miguel E. (2018), “Registered Reports: Piloting a Pre-Results Review Process at the Journal of Development Economics,” World Bank Development Impact Blog, URL = https://blogs.worldbank.org/impactevaluations/registered-reportspiloting-pre-results-review-process-journal-development-economics [Accessed 02.07.2020].
View in Google Scholar
Frankel A., Kasy M. (2020), “Which Findings should be Published?,” URL = https://maxkasy.github.io/home/files/papers/findings.pdf [Accessed 25.08.2020].
View in Google Scholar
Frisch M. (2015), “Predictivism and Old Evidence: A Critical Look at Climate Model Tuning,” European Journal for Philosophy of Science 5 (2): 171–190.
View in Google Scholar
DOI: https://doi.org/10.1007/s13194-015-0110-4
Glymour C. (1980), Theory and Evidence, Princeton University Press, Princeton, N.J.
View in Google Scholar
Head M.L., Holman L., Lanfear R. et al. (2015), “The Extent and Consequences of P-Hacking in Science,” PLoS Biology 13 (3): e1002106.
View in Google Scholar
DOI: https://doi.org/10.1371/journal.pbio.1002106
Howson C. (1991), “The ‘Old Evidence’ Problem,” The British Journal for the Philosophy of Science 42 (4): 547–555.
View in Google Scholar
DOI: https://doi.org/10.1093/bjps/42.4.547
Howson C., Franklin A. (1991), “Maher, Mendeleev and Bayesianism,” Philosophy of Science 58 (4): 574–585.
View in Google Scholar
DOI: https://doi.org/10.1086/289641
Humphreys M., Sanchez De la Sierra R., Windt P.V.D. (2013), “Fishing, Commitment, and Communication: A Proposal for Comprehensive Nonbinding Research Registration,” Political Analysis 21 (1): 1–20.
View in Google Scholar
DOI: https://doi.org/10.1093/pan/mps021
Ioannidis J.P.A. (2005), “Why Most Published Research Findings are False,” PLoS Medicine 2 (8): e124.
View in Google Scholar
DOI: https://doi.org/10.1371/journal.pmed.0020124
Ioannidis J.P.A. (2008), “Why Most Discovered True Associations are Inflated,” Epidemiology 19 (5): 640–648.
View in Google Scholar
DOI: https://doi.org/10.1097/EDE.0b013e31818131e7
Leamer E.E. (1983), “Let’s Take The Con out of Econometrics,” American Economic Review 73 (1): 31–43.
View in Google Scholar
Leonelli S. (2016), Data-Centric Biology: A Philosophical Study, University of Chicago Press, Chicago.
View in Google Scholar
DOI: https://doi.org/10.7208/chicago/9780226416502.001.0001
Libgober J. (Forthcoming), “False Positives and Transparency,” American Economic Journal: Microeconomics, URL = https://www.aeaweb.org/articles?id=10.1257/mic.20190218 [Accessed 01.10.2020].
View in Google Scholar
Maher P. (1988), “Prediction, Accommodation, and the Logic of Discovery,” [in:] PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, vol. 1, A. Fine, J. Leplin (eds.), Philosophy of Science Association, East Lansing, MI: 273–285.
View in Google Scholar
Mayo D.G. (1996), Error and the Growth of Experimental Knowledge, University of Chicago Press, Chicago.
View in Google Scholar
DOI: https://doi.org/10.7208/chicago/9780226511993.001.0001
Miguel E., Camerer C., Casey K. et al. (2014), “Promoting Transparency in Social Science Research,” Science 343 (6166): 30–31.
View in Google Scholar
DOI: https://doi.org/10.1126/science.1245317
Nosek B.A., Lakens D. (2014), “Registered Reports: A Method to Increase the Credibility of Published Results,” Social Psychology 45 (3): 137–141.
View in Google Scholar
DOI: https://doi.org/10.1027/1864-9335/a000192
Olken B.A. (2015), “Promises and Perils of Pre-Analysis Plans,” Journal of Economic Perspectives 29 (3): 61–80.
View in Google Scholar
DOI: https://doi.org/10.1257/jep.29.3.61
Pagan A. (1987), “Three Econometric Methodologies: A Critical Appraisal,” Journal of Economic Surveys 1 (1–2): 3–23.
View in Google Scholar
DOI: https://doi.org/10.1111/j.1467-6419.1987.tb00022.x
Phillips P.C.B. (1988), “Reflections on Econometric Methodology,” Economic Record 64 (4): 344–359.
View in Google Scholar
DOI: https://doi.org/10.1111/j.1475-4932.1988.tb02075.x
Pearson T.A., Manolio T.A. (2008), “How to Interpret a Genome-Wide Association Study,” Journal of the American Medical Association 299 (11): 1335–1344.
View in Google Scholar
DOI: https://doi.org/10.1001/jama.299.11.1335
Simmons J.P., Nelson L.D., Simonsohn U. (2011), “False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant,” Psychological Science 22 (11): 1359–1366.
View in Google Scholar
DOI: https://doi.org/10.1177/0956797611417632
Sober E. (2015), Ockham’s Razors. A User’s Manual, Cambridge University Press, Cambridge.
View in Google Scholar
DOI: https://doi.org/10.1017/CBO9781107705937
White R. (2003), “The Epistemic Advantage of Prediction over Accommodation,” Mind 112 (448): 653–683.
View in Google Scholar
DOI: https://doi.org/10.1093/mind/112.448.653
Worrall J. (2014), “Prediction and Accommodation Revisited,” Studies in History and Philosophy of Science Part A 45 (1): 54–61.
View in Google Scholar
DOI: https://doi.org/10.1016/j.shpsa.2013.10.001