Alsheikh-Ali, A. A., Qureshi, W., Al-Mallah, M. H., & Ioannidis, J. P. A. (2011). Public availability of published research data in high-impact journals. PLoS ONE, 6(9), e24357. https://doi.org/10.1371/journal.pone.0024357CrossRefGoogle ScholarPubMed

Anderson, M., & Magruder, J. (2017). Split-sample strategies for avoiding false discoveries. w23544. Cambridge, MA: National Bureau of Economic Research. doi:10.3386/w23544Google Scholar

Anderson, S. F., Kelley, K., & Maxwell, S. E. (2017). Sample-size planning for more accurate statistical power: A method adjusting sample effect sizes for publication bias and uncertainty. Psychological Science, 28(11), 1547–1562. doi:10.1177/0956797617723724Google Scholar

Begley, C. G., & Ellis, L. M. (2012). Drug development: Raise standards for preclinical cancer research. Nature, 483(7391), 531–533. doi:10.1038/483531aGoogle Scholar

Bem, D. J. (2004). Writing the empirical journal article. In Zanna, M. P. & Darley, J. M. (Eds.), The compleat academic: A practical guide for the beginning social scientist (2nd edn., pp. 185–219). Washington, DC: American Psychological Association.Google Scholar

Board of Governors of the Federal Reserve System, Chang, A. C., & Li, P. (2015). Is economics research replicable? Sixty published papers from thirteen journals say “usually not.” Finance and Economics Discussion Series, 2015(83), 1–26. doi:10.17016/FEDS.2015.083Google Scholar

Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, , Emma, S. J., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365–376. doi:10.1038/nrn3475CrossRefGoogle ScholarPubMed

Claerbout, J. (1994). Seventeen years of super computing and other problems in seismology. Paper presented at the National Research Council meeting on High Performance Computing in Seismology. http://sepwww.stanford.edu/sep/jon/nrc.htmlGoogle Scholar

Crampton, E. W. (1947). The growth of the odontoblast of the incisor teeth as a criterion of vitamin C intake of the guinea pig. Journal of Nutrition, 33(5), 491–504. http://jn.nutrition.org/content/33/5/491.full.pdfCrossRefGoogle ScholarPubMed

de Groot, A. (1969). Methodology: Foundations of inference and research in the behavioral sciences. The Hague: Mouton.Google Scholar

Franco, A., Malhotra, N., & Simonovits, G. (2014). Publication bias in the social sciences: Unlocking the file drawer. Science, 345(6203) 1502–1505. doi:10.1126/science.1255484Google Scholar

Hoenig, J. M., & Heisey, D. M. (2001). The abuse of power. American Statistician, 55(February), 19–24. doi:10.1198/000313001300339897Google Scholar

Kerr, N. L. (1998). HARKing: Hypothesizing after the results are known. Personality and Social Psychology Review, 2(3), 196–217. doi:10.1207/s15327957pspr0203_4CrossRefGoogle ScholarPubMed

Lakens, D. (2014a). Performing high-powered studies efficiently with sequential analyses: Sequential analyses. European Journal of Social Psychology, 44(7), 701–710. doi:10.1002/ejsp.2023CrossRefGoogle Scholar

Lakens, D. (2014b, December 19). The 20% statistician: Observed power, and what to do if your editor asks for post-hoc power analyses. Retrieved November 11, 2017, from https://web.archive.org/web/20170711191030/http://daniellakens.blogspot.com/2014/12/observed-power-and-what-to-do-if-your.htmlGoogle Scholar

Lakens, (2017). Equivalence tests: A practical primer for tests, correlations, and meta-analyses. Social Psychological and Personality Science, 8(4), 355–362. doi:10.1177/1948550617697177CrossRefGoogle ScholarPubMed

Lin, W., & Green, D. P. (2016). Standard operating procedures: A safety net for pre-analysis plans. PS: Political Science & Politics, 49(3), 495–500. doi:10.1017/S1049096516000810Google Scholar

Lindsay, S., Simons, D., & Lilienfeld, S. (2016). Research preregistration 101. Observer, 29(10), 14–16.Google Scholar

Lithgow, G. J., Driscoll, M., & Phillips, P. (2017). A long journey to reproducible results. Nature, 548(7668) 387–388. doi:10.1038/548387aGoogle Scholar

Nosek, B. A., Alter, G., Banks, G. C., Borsboom, D., Bowman, S. D., Breckler, S. J., … Yarkoni, T. (2015). Promoting an open research culture. Science, 348(6242), 1422–1425. doi:10.1126/science.aab2374CrossRefGoogle ScholarPubMed

Open Science Collaboration (2015). Estimating the reproducibility of psychological science. Science, 349(6251), aac4716. doi:10.1126/science.aac4716Google Scholar

Pashler, H., & Wagenmakers, E.-J. (2012). Editors’ introduction to the special section on replicability in psychological science: A crisis of confidence? Perspectives on Psychological Science, 7(6), 528–530. doi:10.1177/1745691612465253CrossRefGoogle Scholar

Pereira, T. V., & Ioannidis, J. P. A. (2011). Statistically significant meta-analyses of clinical trials have modest credibility and inflated effects. Journal of Clinical Epidemiology, 64(10), 1060–1069. doi:10.1016/j.jclinepi.2010.12.012Google Scholar

Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86, 638–641. doi:10.1037/0033-2909.86.3.638Google Scholar

Savage, C. J., & Vickers, A. J. (2009). Empirical study of data sharing by authors publishing in PLoS journals. Ed. Chris Mavergames. PLoS ONE, 4(9), e7078. doi:10.1371/journal.pone.0007078Google Scholar

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. doi:10.1177/0956797611417632CrossRefGoogle ScholarPubMed

Simons, D. J., Shoda, Y., & Lindsay, D. S. (2017). Constraints on generality (COG): A proposed addition to all empirical papers. Perspectives on Psychological Science, August, 174569161770863. doi:10.1177/1745691617708630Google Scholar

Vazire, S. (2017). Quality uncertainty erodes trust in Science. Collabra: Psychology, 3(1), 1. doi:10.1525/collabra.74CrossRefGoogle Scholar

Vazire, S. (2018). Implications of the credibility revolution for productivity, creativity, and progress. Perspectives on Psychological Science, 13(4), 411–17.Google Scholar

Veer, A. E. van 't, & Giner-Sorolla, R. (2016). Pre-registration in social psychology – A discussion and suggested template. Journal of Experimental Social Psychology, 67(November), 2–12. doi:10.1016/j.jesp.2016.03.004Google Scholar

Vines, T. H., Albert, A. Y. K., Andrew, R. L., Débarre, F., Bock, D. G., Franklin, M. T., … Rennison, D. J. (2014). The availability of research data declines rapidly with article age. Current Biology, 24(1), 94–97. doi.org/10.1016/j.cub.2013.11.014Google Scholar

Wagenmakers, E.-J., Dutilh, G., & Sarafoglou, A. (2018). The creativity-verification cycle in psychological science: New methods to combat old idols. PsyArXiv. doi:10.17605/OSF.IO/37NTPGoogle Scholar

Wagenmakers, E.-J., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Selker, R., et al. (2017). Bayesian inference for psychology. Part II: Example Applications with JASP. Psychonomic Bulletin & Review, July. doi:10.3758/s13423-017-1323-7Google Scholar

Wicherts, J. M., Borsboom, D., Kats, J., & Molenaar, D. (2006). The poor availability of psychological research data for reanalysis. American Psychologist, 61(7), 726–728. doi:10.1037/0003-066X.61.7.72Google Scholar