1. Brembs, B. (2018). Prestigious Science Journals Struggle to Reach Even Average Reliability. Frontiers in Human Neuroscience, 12.
2. Breznau, N. et al. (2022). Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty. Proceedings of the National Academy of Sciences, 119(44):e2203150119.
3. Camerer, C. 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.
4. Feigenbaum, S. and Levy, D. M. (1996). The technological obsolescence of scientific fraud. Rationality and Society, 8(3):261–276.
5. Gelman, A. and Loken, E. (2014). The statistical crisis in science. American Scientist, 102(6):460–466.
6. Gelman, A. and Tuerlinckx, F. (2000). Type S error rates for classical and Bayesian single and multiple comparison procedures. Computational Statistics, 15(3):373–390.
7. Gignac, G. E. and Zajenkowski, M. (2020). The Dunning-Kruger effect is (mostly) a statistical artefact: Valid approaches to testing the hypothesis with individual differences data. Intelligence, 80:101449.
8. Goodman, S. N., Fanelli, D., and Ioannidis, J. P. A. (2016). What does research reproducibility mean? Science Translational Medicine, 8(341):341ps12–341ps12.
9. Gordon, M. et al. (2020). Are replication rates the same across academic fields? Community forecasts from the DARPA SCORE programme. Royal Society Open Science, 7(7):200566.
10. Head, M. L., Holman, L., Lanfear, R., Kahn, A. T., and Jennions, M. D. (2015). The extent and consequences of p-hacking in science. PLoS biology, 13(3):e1002106.
11. Imbens, G. W. (2021). Statistical Significance, p-Values, and the Reporting of Uncertainty. Journal of Economic Perspectives, 35(3):157–174.
12. Ioannidis, J. P. A. (2005). Why Most Published Research Findings Are False. PLOS Medicine,2(8):e124.
13. Jansen, R. A., Rafferty, A. N., and Griffiths, T. L. (2021). A rational model of the Dunning-Kruger effect supports insensitivity to evidence in low performers. Nature Human Behaviour, 5(6):756–763.
14. Kleinfeld, J. S. (2002). The small world problem. Society, 39(2):61–66.
15. Krueger, J. and Mueller, R. A. (2002). Unskilled, unaware, or both? The better-than-average heuristic and statistical regression predict errors in estimates of own performance. Journal of personality and social psychology, 82(2):180.
16. Kruger, J. and Dunning, D. (1999). Unskilled and unaware of it: how difficulties in recognizing one’s own incompetence lead to inflated self-assessments. Journal of personality and social psychology, 77(6):1121.
17. Lakatos, I. (1976). Falsification and the Methodology of Scientific Research Programmes. In Harding, S. G., editor, Can Theories be Refuted? Essays on the Duhem-Quine Thesis, Synthese Library, pages 205–259. Springer Netherlands, Dordrecht.
18. Milgram, S. (1967). The small world problem. Psychology today, 2(1):60–67.
19. Nissen, S. B., Magidson, T., Gross, K., and Bergstrom, C. T. (2016). Publication bias and the canonization of false facts. eLife, 5:e21451. Publisher: eLife Sciences Publications, Ltd.
20. Nosek, B. A., Ebersole, C. R., DeHaven, A. C., and Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11):2600–2606.
21. OPEN SCIENCE COLLABORATION (2015). Estimating the reproducibility of psychological science. Science, 349(6251):aac4716.
22. Patel, C. J., Burford, B., and Ioannidis, J. P. A. (2015). Assessment of vibration of effects due to model specification can demonstrate the instability of observational associations. Journal of Clinical Epidemiology, 68(9):1046–1058.
23. Rubin, M. (2017). When Does HARKing Hurt? Identifying When Different Types of Undisclosed Post Hoc Hypothesizing Harm Scientific Progress. Review of General Psychology, 21(4):308–320.
24. Schmidt, S. (2009). Shall we Really do it Again? The Powerful Concept of Replication is Neglected in the Social Sciences. Review of General Psychology, 13(2):90–100.
25. Simmons, J. P., Nelson, D., and Simonsohn, U. (2021). Pre-registration: Why and How. Journal of Consumer Psychology, 31(1):151–162.
26. Simmons, J. P., Nelson, L. D., and Simonsohn, U. (2011). False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychological Science, 22(11):1359–1366.
27. Simonsohn, U., Simmons, J. P., and Nelson, L. D. (2020). Specification curve analysis. Nature Human Behaviour, 4(11):1208–1214. Number: 11 Publisher: Nature Publishing Group.
28. Steegen, S., Tuerlinckx, F., Gelman, A., and Vanpaemel, W. (2016). Increasing Transparency Through a Multiverse Analysis. Perspect Psychol Sci, 11(5):702–712.
29. Szucs, D. and Ioannidis, J. P. A. (2017). Empirical assessment of published effect sizes and power in the recent cognitive neuroscience and psychology literature. PLOS Biology, 15(3):e2000797.
30. Travers, J. and Milgram, S. (1969). An Experimental Study of the Small World Problem. Sociometry, 32(4):425–443.
31. van Zwet, E. W. and Cator, E. A. (2021). The significance filter, the winner’s curse and the need to shrink. Statistica Neerlandica, 75(4):437–452.
32. Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of small-world networks. nature, 393(6684):440–442.
33. West, J. D. and Bergstrom, C. T. (2021). Misinformation in and about science. Proceedings of the National Academy of Sciences, 118(15):e1912444117.
34. Yamada, Y. (2018). How to Crack Pre-registration: Toward Transparent and Open Science. Frontiers in Psychology, 9.
35. Young, C. and Holsteen, K. (2017). Model Uncertainty and Robustness: A Computational Framework for Multimodel Analysis. Sociological Methods & Research, 46(1):3–40.

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