References
- Bayarri, M. J., and Berger, J. O. (2004), “The Interplay of Bayesian and Frequentist Analysis,” Statistical Science, 19, 58–80. DOI: https://doi.org/10.1214/088342304000000116.
- Benjamin, D. J., and Berger, J. O. (2019), “Three Recommendations for Improving the Use of p-Values,” The American Statistician, 73, 186–191. DOI: https://doi.org/10.1080/00031305.2018.1543135.
- Berger, J. O. (2003), “Could Fisher, Jeffreys and Neyman Have Agreed on Testing?” (with discussion), Statistical Science, 18, 1–32. DOI: https://doi.org/10.1214/ss/1056397485.
- Berger, J. O., and Delampady, M. (1987), “Testing Precise Hypotheses,” Statistical Science, 2, 317–335. DOI: https://doi.org/10.1214/ss/1177013238.
- Berger, J. O., and Sellke, T. (1987), “Testing a Point Null Hypothesis: The Irreconcilability of p Values and Evidence,” Journal of the American Statistical Association, 82, 112–122. DOI: https://doi.org/10.2307/2289131.
- Betensky, R. A. (2019), “The p-Value Requires Context, Not a Threshold,” The American Statistician, 73, 115–117. DOI: https://doi.org/10.1080/00031305.2018.1529624.
- Briggs, W. M. (2017), “The Substitute for p-Values,” Journal of the American Statistical Association, 112, 897–898. DOI: https://doi.org/10.1080/01621459.2017.1311264.
- Billheimer, D. (2019), “Predictive Inference and Scientific Reproducibility,” The American Statistician, 73, 291–295. DOI: https://doi.org/10.1080/00031305.2018.1518270.
- Casella, G., and Berger, R. L. (1987), “Reconciling Bayesian and Frequentist Evidence in the One-Sided Testing Problem” (with discussion), Journal of the American Statistical Association, 82, 106–111. DOI: https://doi.org/10.1080/01621459.1987.10478396.
- Colquhoun, D. (2014), “An Investigation of the False Discovery Rate and the Misinterpretation of p-Values,” Royal Society of Open Science, 1, 140–216.
- Concato, J., and Hartigan, J. A. (2016), “P Values: From Suggestion to Superstition,” Journal of Investigative Medicine, 64, 1166–1171. DOI: https://doi.org/10.1136/jim-2016-000206.
- Cumming, G. (2014), “The New Statistics: Why and How,” Psychological Science, 25, 7–29. DOI: https://doi.org/10.1177/0956797613504966.
- Donahue, R. M. J. (1999), “A Note on Information Seldom Reported via the P Value,” The American Statistician, 53, 303–306. DOI: https://doi.org/10.2307/2686048.
- Dudley, R. M., and Haughton, D. (2002), “Asymptotic Normality With Small Relative Errors of Posterior Probabilities of Half-Spaces,” The Annals of Statistics, 30, 1311–1344. DOI: https://doi.org/10.1214/aos/1035844978.
- Fidler, F., Thomason, N., Cumming, G., Finch, S., Leeman, J. (2004), “Editors Can Lead Researchers to Confidence Intervals, But Can’t Make Them Think: Statistical Reform Lessons From Medicine,” Psychological Science, 15, 119–126. DOI: https://doi.org/10.1111/j.0963-7214.2004.01502008.x.
- Gill, J. (2018), “Comments From the New Editor,” Political Analysis, 26, 1–2. DOI: https://doi.org/10.1017/pan.2017.41.
- Goodman, S. N. (1999), “Toward Evidence-Based Medical Statistics. 1: The p Value Fallacy,” Annals of Internal Medicine, 130, 995–1004. DOI: https://doi.org/10.7326/0003-4819-130-12-199906150-00008.
- Hubbard, R., and Lindsay, R. M. (2008), “Why P Values Are Not a Useful Measure of Evidence in Statistical Significance Testing,” Theory & Psychology, 18, 69–88. DOI: https://doi.org/10.1177/0959354307086923.
- Hung, H. J., O’Neill, R. T., Bauer, P., and Kohne, K. (1997), “The Behavior of the p-Value When the Alternative Hypothesis Is True,” Biometrics, 53, 11–22. DOI: https://doi.org/10.2307/2533093.
- Ioannidis, J. P. (2005), “Why Most Published Research Findings Are False,” PLoS Medicine, 2, 124. DOI: https://doi.org/10.1371/journal.pmed.0020124.
- Jager, L. R., and Leek, J. T. (2014), “An Estimate of the Science-Wise False Discovery Rate and Application to the Top Medical Literature,” Biostatistics, 15, 1–12. DOI: https://doi.org/10.1093/biostatistics/kxt007.
- Johnson, V. E. (2013), “Revised Standards for Statistical Evidence,” Proceedings of the National Academy of Sciences of the United States of America, 110, 19313–19317. DOI: https://doi.org/10.1073/pnas.1313476110.
- Leek, J., McShane, B. B., Gelman, A., Colquhoun, D., Nuijten, M. B., and Goodman, S. N. (2017), “Five Ways to Fix Statistics,” Nature, 551, 557–559. DOI: https://doi.org/10.1038/d41586-017-07522-z.
- Lehmann, E. L., and Romano, J. P. (2005), Testing Statistical Hypotheses, New York: Springer.
- Lindley, D. V. (1957), “A Statistical Paradox,” Biometrika, 44, 187–192. DOI: https://doi.org/10.1093/biomet/44.1-2.187.
- Manski, C. F. (2019), “Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis Testing,” The American Statistician, 73, 296–304. DOI: https://doi.org/10.1080/00031305.2018.1513377.
- Matthews, R. A. J. (2019), “Moving Towards the Post p < 0.05 Era via the Analysis of Credibility,” The American Statistician, 73, 202–212.
- McShane, B. B., Gal, D., Gelman, A., Robert, C., and Tackett, J. L. (2019), “Abandon Statistical Significance,” The American Statistician, 73, 235–245. DOI: https://doi.org/10.1080/00031305.2018.1527253.
- Murtaugh, P. A. (2014), “In Defense of P Values,” Ecology, 95, 611–617. DOI: https://doi.org/10.1890/13-0590.1.
- Nuzzo, R. (2014), “Statistical Errors: P Values, the ‘Gold Standard’ of Statistical Validity, Are Not as Reliable as Many Scientists Assume,” Nature, 506, 150–152.
- Pratt, J. W. (1965), “Bayesian Interpretation of Standard Inference Statements” (with discussion), Journal of the Royal Statistical Society, Series B, 27, 169–203. DOI: https://doi.org/10.1111/j.2517-6161.1965.tb01486.x.
- Ranstam, J. (2012), “Why the p-Value Culture Is Bad and Confidence Intervals a Better Alternative,” Osteoarthritis Cartilage, 20, 805–808. DOI: https://doi.org/10.1016/j.joca.2012.04.001.
- Rosenthal, R., and Rubin, D. B. (1983), “Ensemble-Adjusted p Values,” Psychological Bulletin, 94, 540–541. DOI: https://doi.org/10.1037/0033-2909.94.3.540.
- Royall, R. M. (1986), “The Effect of Sample Size on the Meaning of Significance Tests,” The American Statistician, 40, 313–315. DOI: https://doi.org/10.2307/2684616.
- Rubin, D. B. (1984), “Bayesianly Justifiable and Relevant Frequency Calculations for the Applies Statistician,” The Annals of Statistics, 12, 1151–1172. DOI: https://doi.org/10.1214/aos/1176346785.
- Rubin, D. B. (1998), “More Powerful Randomization-Based p-Values in Double-Blind Trials With Non-Compliance,” Statistics in Medicine, 17, 371–385.
- Sackrowitz, H., and Samuel-Cahn, E. (1999), “P Values as Random Variable-Expected P Values,” The American Statistician, 53, 326–331. DOI: https://doi.org/10.2307/2686051.
- Savalei, V., and Dunn, E. (2015), “Is the Call to Abandon p-Values the Red Herring of the Replicability Crisis?,” Frontiers in Psychology, 6, 245. DOI: https://doi.org/10.3389/fpsyg.2015.00245.
- Schervish, M. J. (1996), “P Values: What They Are and What They Are Not,” The American Statistician, 50, 203–206. DOI: https://doi.org/10.2307/2684655.
- Sellke, T., Bayarri, M. J., and Berger, J. O. (2001), “Calibration of p-Values for Testing Precise Null Hypotheses,” The American Statistician, 55, 62–71. DOI: https://doi.org/10.1198/000313001300339950.
- 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, 1359–1366. DOI: https://doi.org/10.1177/0956797611417632.
- Trafimow, D., Amrhein, V., Areshenkoff, C. N., Barrera-Causil, C. J., Beh, E. J., Bilgiç, Y. K., Bono, R., Bradley, M. T., Briggs, W. M., Cepeda-Freyre, H. A., and Chaigneau, S. E. (2018), “Manipulating the Alpha Level Cannot Cure Significance Testing,” Frontiers in Psychology, 9, 699. DOI: https://doi.org/10.3389/fpsyg.2018.00699.
- Trafimow, D., and Marks, M. (2015), “Editorial,” Basic and Applied Social Psychology, 37, 1–2. DOI: https://doi.org/10.1080/01973533.2015.1012991.
- Wagenmakers, E. J. (2007), “A Practical Solution to the Pervasive Problems of p Values,” Psychonomic Bulletin & Review, 14, 779–804. DOI: https://doi.org/10.3758/BF03194105.
- Wasserstein, R. L., and Lazar, N. A. (2016), “The ASA’s Statement on p-Values: Context, Process, and Purpose,” The American Statistician, 70, 129–133. DOI: https://doi.org/10.1080/00031305.2016.1154108.