Do we really need confidence intervals in the new statistics?

References

• Bakan, D. (1966). The test of significance in psychological research. Psychological Bulletin, 77, 423–437.
   Web of Science ®Google Scholar
• Berger, J., & Sellke, T. (1987). Testing a point null hypothesis: The irreconcilability of P values and evidence (with comments). Journal of the American Statistical Association, 82(1), 112–139.
   Google Scholar
• Berkson, J. (1938). Some difficulties of interpretation encountered in the application of the chi-square test. Journal of the American Statistical Association, 33, 526–536.
   Google Scholar
• Boring, E. (1919). Mathematical vs. scientific importance. Psychological Bulletin, 16, 335–338.
   Google Scholar
• Brunton-Smith, I., Carpenter, J., Kenward, M., & Tarling, R. (2014). Multiple imputation for handling missing data in social research. Social Research Update, 65(Autumn), 2014.
   Google Scholar
• Carver, R. (1978). The case against statistical significance testing, harvard educational review. 48, 378-399. doi:10.17763/haer.48.3.t490261645281841
   Google Scholar
• Chowl, J., & Ekholm, E. (2018) Do published studies yield larger effect sizes than unpublished studies in education and special education? Educational Psychology Review, 30:727–744,
   Google Scholar
• Coe, R. (2002) It’s the effect size, stupid: What effect size is and why it is important, Paper presented at the British Educational Research Association annual conference, Exeter, 12 September, http://www.cem.org/attachments/ebe/ESguide.pdf
   Google Scholar
• Colquoun, D. (2014) An investigation of the false discovery rate and the misinterpretation of p-values, Royal Society Open Science, http://rsos.royalsocietypublishing.org/content/1/3/140216
   Google Scholar
• Colquoun, D. (2016) The problem with p-values, Aeon, https://aeon.co/essays/it-s-time-for-science-to-abandon-the-term-statistically-significant
   Google Scholar
• Cumming, G. (2014). The new statistics: Why and how. Psychological Science, 25(1), 7–29.
   PubMed Web of Science ®Google Scholar
• Daniel, L. (1998). Statistical significance testing: A historical overview of misuse and misinterpretation with implications for the editorial policies of educational journals. Research in the Schools, 5(2), 23–32.
   Google Scholar
• Falk, R., & Greenbaum, C. (1995). Significance tests die hard: The amazing persistence of a probabilistic misconception. Theory and Psychology, 5, 75–98.
   Web of Science ®Google Scholar
• Fidler, F., Thomson, 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(2), 119–126.
   PubMed Web of Science ®Google Scholar
• Filho, D., Paranhos, R., Da Rocha, E., Batista, M., Da Silva, J., Santos, M., & Marino, J. (2013) When is statistical significance not significant? http://www.scielo.br/pdf/bpsr/v7n1/02.pdf
   Google Scholar
• Frank, K., Maroulis, S., Doung, M., & Kelcey, B. (2013). What would it take to change an inference? Using Rubin’s causal model to interpret the robustness of causal inferences. Educational Evaluation and Policy Analysis, 35(4), 437–460.
   Web of Science ®Google Scholar
• Freedman, D. (2004). Sampling. In M. Lewis-Beck, A. Bryman, & T. Liao (Eds.), Sage encyclopaedia of social science research methods (pp. 987–991). Thousand Oaks, CA: Sage.
   Google Scholar
• Glass, G. (2014). Random selection, random assignment and Sir Ronald Fisher. Psychology of Education Review, 38(1), 12–13.
   Google Scholar
• Gorard, S. (1999). Keeping a sense of proportion: the ‘politician’s error’ in analysing school outcomes. British Journal of Educational Studies, 47(3), 235–246. doi:10.1111/1467-8527.00116
   Web of Science ®Google Scholar
• Gorard, S. (2006). Towards a judgement‐based statistical analysis. British Journal of Sociology of Education, 27 (1), 67-80. doi:10.1080/01425690500376663
   Web of Science ®Google Scholar
• Gorard, S. (2014a). A proposal for judging the trustworthiness of research findings. Radical Statistics, 110, 47-60, http://www.radstats.org.uk/no110/Gorard110.pdf
   Google Scholar
• Gorard, S. (2014b). Confidence intervals, missing data and imputation. International Journal of Research in Methodology Educational, 5(3),693-698. http://cirworld.org/journals/index.php/ijrem/article/view/2105/pdf_51
   Google Scholar
• Gorard, S. (2015). Introducing the mean absolute deviation ‘effect’ size. International Journal of Research & Method in Education, 38(2), 290-114. doi:10.1080/1743727X.2014.920810.
   Web of Science ®Google Scholar
• Gorard, S., & Gorard, J. (2016). What to do instead of significance testing? calculating the ‘number of counterfactual cases needed to disturb a finding’. International Journal of Social Research Methodology, 19(4), 481–489. doi:10.1080/13645579.2015.1091235
   Web of Science ®Google Scholar
• Gorard, S., See, BH., & Siddiqui, N. (2017). The trials of evidence-based education. London: Routledge.
   Google Scholar
• Greenland, S., Senn, S., Rothman, K., Carlin, J., Poole, C., Goodman, S., & Altman, D. (2016). Statistical tests, P-values, confidence intervals, and power: A guide to misinterpretations. European Journal of Epidemiology, 31(4), 337–350.
   PubMed Web of Science ®Google Scholar
• Guttman, L. (1985). The illogic of statistical inference for cumulative science. Applied Stochastic Models and Data Analysis, 1, 3–10.
   Google Scholar
• Halsey, L., Curran-Everett, D., Vowler, S., & Drummond, G. (2015). The fickle p value generates irreproducible results. Nature Methods, 12(3), 179–185.
   PubMed Web of Science ®Google Scholar
• Hedges, L., & Olkin, I. (2014). Statistical methods for meta-analysis. Orlando: Academic Press Inc.
   Google Scholar
• Hubbard, R., & Meyer, C. (2013). The rise of statistical significance testing in public administration research and why this is a mistake. Journal of Business and Behavioral Sciences, 25, 1.
   Google Scholar
• Hunter, J. (1997). Needed: A Ban on the Significance Test. Psychological Science, 8(1), 3–7. The University of Western Ontario Series in Philosophy of Science Volume 6b
   Web of Science ®Google Scholar
• Kline, R. (2004). Beyond significance testing: Reforming data analysis methods in behavioral research. Washington, DC: American Psychological Association.
   Google Scholar
• Lindley, D., & Phillips, L. (1976). Inference for a Bernoulli process. The American Statistician, 30(3), 112–119.
   Web of Science ®Google Scholar
• Loftus, G. (1991). On the tyranny of hypothesis testing in the social sciences. Contemporary Psychology, 36, 102–105.
   Web of Science ®Google Scholar
• Matthews, R. (2001). Methods for assessing the credibility of clinical trial outcomes. Drug Information Journal, 35, 1469–1478.
   Google Scholar
• Meehl, P. (1967). Theory – testing in psychology and physics: A methodological paradox. Philosophy of Science, 34, 103–115.
   Web of Science ®Google Scholar
• Morey, R., Hoekstra, R., Rouder, J., Lee, M., & Wagenmakers, E. (2016). The fallacy of placing confidence in confidence intervals. Psychonomic Bulletin & Review, 23(1), 103–123.
   PubMed Web of Science ®Google Scholar
• Morrison, D., & Henkel, R. (1969). Significance tests reconsidered. American Sociologist, 4, 131–140.
   Google Scholar
• Nickerson, R. (2000). Null hypothesis significance testing: A review of an old and continuing controversy. Psychological Methods, 5(2), 241–301.
   PubMed Web of Science ®Google Scholar
• Nix, T., & Barnette, J. (1998). The data analysis dilemma: Ban or abandon, a review of null hypothesis significance testing. Research in the Schools, 5(2), 3–14.
   Google Scholar
• Pannell, D. (1997). Sensitivity analysis of normative economic models: Theoretical framework and practical strategies. Agricultural Economics, 16, 139–152.
   Web of Science ®Google Scholar
• Pharoah, P., Jones, M., & Kar, S. (2017). P-values and confidence intervals: Not fit for purpose? BioRxiv. doi:10.1101/180117
   Google Scholar
• Rozeboom, W. (1960). The fallacy of the null hypothesis significance test. Psychological Bulletin, 57, 416-428.
   PubMed Web of Science ®Google Scholar
• Selke, T., Bayarri, M., & Berger, J. (2001). Calibration of p values for testing precise null hypotheses. The American Statistician, 55(1), 62–71.
   Web of Science ®Google Scholar
• Starbuck, W. (2016). 60th anniversary essay: how journals could improve research practices in social science. Administrative Science Quarterly, 61(61(61))(2) 2), 165–183. doi:10.1177/0001839216629644
   Web of Science ®Google Scholar
• Thabane, L., Mbuagbaw, L., Zhang, S., Samaan, Z., Marcucci, M., Ye, C., Thabane, M., et al. (2013). A tutorial on sensitivity analyses in clinical trials. BMC Medical Research Methodology, 13, 92. http://www.biomedcentral.com/1471-2288/13/92
   PubMed Web of Science ®Google Scholar
• Tryon, W. (1998). The inscrutable null hypothesis. American Psychologist, 53, 796.
   Web of Science ®Google Scholar
• Walster, G., & Cleary, T. (1970). A proposal for a new editorial policy in the social sciences. The American Statistician, 241, 16–19.
   Google Scholar
• Watts, D. (1991). Why is introductory statistics difficult to learn? The American Statistician, 45(4), 290–291.
   Web of Science ®Google Scholar
• White, P, & Gorard, S. (2017). Against inferential statistics: how and why current statistics teaching gets it wrong. Statistics Education Research Journal, 16(1), 55-65.
   Google Scholar