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NTIS 바로가기The American statistician, v.73 suppl.1, 2019년, pp.1 - 19
Wasserstein, Ronald L. (American Statistical Association, Alexandria, VA) , Schirm, Allen L. (Mathematica Policy Research (retired), Washington, DC) , Lazar, Nicole A. (Department of Statistics, University of Georgia, Athens, GA)
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Amrhein, Valentin, Trafimow, David, Greenland, Sander. Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication. The American statistician, vol.73, no.suppl1, 262-270.
Anderson, Andrew A.. Assessing Statistical Results: Magnitude, Precision, and Model Uncertainty. The American statistician, vol.73, no.suppl1, 118-121.
Benjamin, Daniel J., Berger, James O.. Three Recommendations for Improving the Use of p -Values. The American statistician, vol.73, no.suppl1, 186-191.
Betensky, Rebecca A.. The p -Value Requires Context, Not a Threshold. The American statistician, vol.73, no.suppl1, 115-117.
Billheimer, Dean. Predictive Inference and Scientific Reproducibility. The American statistician, vol.73, no.suppl1, 291-295.
Blume, Jeffrey D., Greevy, Robert A., Welty, Valerie F., Smith, Jeffrey R., Dupont, William D.. An Introduction to Second-Generation p -Values. The American statistician, vol.73, no.suppl1, 157-167.
Brownstein, Naomi C., Louis, Thomas A., O’Hagan, Anthony, Pendergast, Jane. The Role of Expert Judgment in Statistical Inference and Evidence-Based Decision-Making. The American statistician, vol.73, no.suppl1, 56-68.
Calin-Jageman, Robert J., Cumming, Geoff. The New Statistics for Better Science: Ask How Much, How Uncertain, and What Else Is Known. The American statistician, vol.73, no.suppl1, 271-280.
Campbell, Harlan, Gustafson, Paul. The World of Research Has Gone Berserk: Modeling the Consequences of Requiring “Greater Statistical Stringency” for Scientific Publication. The American statistician, vol.73, no.suppl1, 358-373.
Colquhoun, David. The False Positive Risk: A Proposal Concerning What to Do About p -Values. The American statistician, vol.73, no.suppl1, 192-201.
Fraser, D. A. S.. The p -value Function and Statistical Inference. The American statistician, vol.73, no.suppl1, 135-147.
Fricker Jr., Ronald D., Burke, Katherine, Han, Xiaoyan, Woodall, William H.. Assessing the Statistical Analyses Used in Basic and Applied Social Psychology After Their p -Value Ban. The American statistician, vol.73, no.suppl1, 374-384.
Gannon, Mark Andrew, de Bragança Pereira, Carlos Alberto, Polpo, Adriano. Blending Bayesian and Classical Tools to Define Optimal Sample-Size-Dependent Significance Levels. The American statistician, vol.73, no.suppl1, 213-222.
Goodman, Steven N.. Why is Getting Rid of P -Values So Hard? Musings on Science and Statistics. The American statistician, vol.73, no.suppl1, 26-30.
Goodman, William M., Spruill, Susan E., Komaroff, Eugene. A Proposed Hybrid Effect Size Plus p -Value Criterion: Empirical Evidence Supporting its Use. The American statistician, vol.73, no.suppl1, 168-185.
Greenland, Sander. Valid P -Values Behave Exactly as They Should: Some Misleading Criticisms of P -Values and Their Resolution With S -Values. The American statistician, vol.73, no.suppl1, 106-114.
The American Statistician Heck P. 73 2019 “Putting the p-Value in Its Place,” Heck, P., and Krueger, J. (2019), “Putting the p-Value in Its Place,” The American Statistician, 73.
Hubbard, Douglas W., Carriquiry, Alicia L.. Quality Control for Scientific Research: Addressing Reproducibility, Responsiveness, and Relevance. The American statistician, vol.73, no.suppl1, 46-55.
Hubbard, Raymond. Will the ASA's Efforts to Improve Statistical Practice be Successful? Some Evidence to the Contrary. The American statistician, vol.73, no.suppl1, 31-35.
Hubbard, Raymond, Haig, Brian D., Parsa, Rahul A.. The Limited Role of Formal Statistical Inference in Scientific Inference. The American statistician, vol.73, no.suppl1, 91-98.
The American Statistician Hurlbert S. 73 2019 Coup de Grâce for a Tough Old Bull: ‘Statistically Significant’ Expires Hurlbert, S., Levine, R., and Utts, J. (2019), “Coup de Grâce for a Tough Old Bull: ‘Statistically Significant’ Expires,” The American Statistician, 73.
Ioannidis, John P. A.. What Have We (Not) Learnt from Millions of Scientific Papers with P Values?. The American statistician, vol.73, no.suppl1, 20-25.
Johnson, Valen E.. Evidence From Marginally Significant t Statistics. The American statistician, vol.73, no.suppl1, 129-134.
Kennedy-Shaffer, Lee. Before p < 0.05 to Beyond p < 0.05: Using History to Contextualize p -Values and Significance Testing. The American statistician, vol.73, no.suppl1, 82-90.
Kmetz, John L.. Correcting Corrupt Research: Recommendations for the Profession to Stop Misuse of p -Values. The American statistician, vol.73, no.suppl1, 36-45.
Lavine, Michael. Frequentist, Bayes, or Other?. The American statistician, vol.73, no.suppl1, 312-318.
Locascio, Joseph J.. The Impact of Results Blind Science Publishing on Statistical Consultation and Collaboration. The American statistician, vol.73, no.suppl1, 346-351.
Manski, Charles F.. Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis Testing. The American statistician, vol.73, no.suppl1, 296-304.
Manski, Charles F., Tetenov, Aleksey. Trial Size for Near-Optimal Choice Between Surveillance and Aggressive Treatment: Reconsidering MSLT-II. The American statistician, vol.73, no.suppl1, 305-311.
Matthews, Robert A. J.. Moving Towards the Post p < 0.05 Era via the Analysis of Credibility. The American statistician, vol.73, no.suppl1, 202-212.
Maurer, Karsten, Hudiburgh, Lynette, Werwinski, Lisa, Bailer, John. Content Audit for p -value Principles in Introductory Statistics. The American statistician, vol.73, no.suppl1, 385-391.
McShane, Blakeley B., Gal, David, Gelman, Andrew, Robert, Christian, Tackett, Jennifer L.. Abandon Statistical Significance. The American statistician, vol.73, no.suppl1, 235-245.
McShane, Blakeley B., Tackett, Jennifer L., Böckenholt, Ulf, Gelman, Andrew. Large-Scale Replication Projects in Contemporary Psychological Research. The American statistician, vol.73, no.suppl1, 99-105.
The American Statistician O’Hagan A 73 2019 10.1080/00031305.2018.1518265 Expert Knowledge Elicitation: Subjective But Scientific O’Hagan, A. (2019), “Expert Knowledge Elicitation: Subjective But Scientific,” The American Statistician, 73.
Pogrow, Stanley. How Effect Size (Practical Significance) Misleads Clinical Practice: The Case for Switching to Practical Benefit to Assess Applied Research Findings. The American statistician, vol.73, no.suppl1, 223-234.
Rose, Sherri, McGuire, Thomas G.. Limitations of P -Values and R -squared for Stepwise Regression Building: A Fairness Demonstration in Health Policy Risk Adjustment. The American statistician, vol.73, no.suppl1, 152-156.
Rougier, Jonathan. p -Values, Bayes Factors, and Sufficiency. The American statistician, vol.73, no.suppl1, 148-151.
Ruberg, Stephen J., Harrell Jr., Frank E., Gamalo-Siebers, Margaret, LaVange, Lisa, Jack Lee, J., Price, Karen, Peck, Carl. Inference and Decision Making for 21st-Century Drug Development and Approval. The American statistician, vol.73, no.suppl1, 319-327.
Steel, E. Ashley, Liermann, Martin, Guttorp, Peter. Beyond Calculations: A Course in Statistical Thinking. The American statistician, vol.73, no.suppl1, 392-401.
Trafimow, David. Five Nonobvious Changes in Editorial Practice for Editors and Reviewers to Consider When Evaluating Submissions in a Post p < 0.05 Universe. The American statistician, vol.73, no.suppl1, 340-345.
Tong, Christopher. Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science. The American statistician, vol.73, no.suppl1, 246-261.
van Dongen, Noah N. N., van Doorn, Johnny B., Gronau, Quentin F., van Ravenzwaaij, Don, Hoekstra, Rink, Haucke, Matthias N., Lakens, Daniel, Hennig, Christian, Morey, Richard D., Homer, Saskia, Gelman, Andrew, Sprenger, Jan, Wagenmakers, Eric-Jan. Multiple Perspectives on Inference for Two Simple Statistical Scenarios. The American statistician, vol.73, no.suppl1, 328-339.
Ziliak, Stephen T.. How Large Are Your G -Values? Try Gosset’s Guinnessometrics When a Little “ p ” Is Not Enough. The American statistician, vol.73, no.suppl1, 281-290.
Boring, Edwin G.. Mathematical vs. scientific significance.. Psychological bulletin, vol.16, no.10, 335-338.
Cumming, Geoff. The New Statistics : Why and How. Psychological science : a journal of the American Psychological Society, vol.25, no.1, 7-29.
Davidian, Marie, Louis, Thomas A.. Why Statistics?. Science, vol.336, no.6077, 12-12.
Journal of the Statistical Society of London Edgeworth F. Y 181 1885 “Methods of Statistics,” Edgeworth, F. Y. (1885), “Methods of Statistics,” Journal of the Statistical Society of London, Jubilee Volume, 181-217.
Rödel, E.. Fisher, R. A.: Statistical Methods for Research Workers, 14. Aufl., Oliver & Boyd, Edinburgh, London 1970. XIII, 362 S., 12 Abb., 74 Tab., 40 s. Biometrische Zeitschrift, vol.13, no.6, 429-430.
European Science Editing Gelman A 13 41 2015 Statistics and Research Integrity, Gelman, A. (2015), “Statistics and Research Integrity,” European Science Editing, 41, 13-14.
The American Statistician, supplemental materials to ASA Statement on p-Values and Statistical Significance Gelman A 1 70 2016 The Problems With p-Values Are Not Just With p-Values Gelman, A. (2016), “The Problems With p-Values Are Not Just With p-Values,” The American Statistician, supplemental materials to ASA Statement on p-Values and Statistical Significance, 70, 1-2.
Gelman, Andrew, Hennig, Christian. Beyond subjective and objective in statistics. Journal of the royal statistical society. Series A, (Statistics in society), vol.180, no.4, 967-1033.
Gelman, Andrew, Stern, Hal. The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant. The American statistician, vol.60, no.4, 328-331.
Ghose T. (2013) “‘Just a Theory’: 7 Misused Science Words ” Scientific American (online) available at https://www.scientificamerican.com/article/just-a-theory-7-misused-science-words/.
Goodman, Steven N.. How sure are you of your result? Put a number on it. Nature, vol.564, no.7734, 7-7.
The Perfect Storm: A True Story of Men Against the Sea Junger S 1997 Junger, S. (1997), The Perfect Storm: A True Story of Men Against the Sea, New York: W.W. Norton.
McShane, Blakeley B., Gal, David. Blinding Us to the Obvious? The Effect of Statistical Training on the Evaluation of Evidence. Management science, vol.62, no.6, 1707-1718.
McShane, Blakeley B., Gal, David. Statistical Significance and the Dichotomization of Evidence. Journal of the American Statistical Association, vol.112, no.519, 885-895.
Mogil, Jeffrey S., Macleod, Malcolm R.. No publication without confirmation. Nature, vol.542, no.7642, 409-411.
Rosenthal, Robert. The file drawer problem and tolerance for null results.. Psychological bulletin, vol.86, no.3, 638-641.
Wasserstein, Ronald L., Lazar, Nicole A.. The ASA Statement on p-Values: Context, Process, and Purpose. The American statistician, vol.70, no.2, 129-133.
Wellek, Stefan. A critical evaluation of the current “p‐value controversy”. Biometrical journal: Biometrische Zeitschrift, vol.59, no.5, 854-872.
The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives Ziliak S. 2008 Ziliak, S., and McCloskey, D. (2008), The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives, Ann Arbor, MI: University of Michigan Press.
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