针对Null Hypothesis Significance Testing (NHST)在统计学理论与实践中所起到的作用/扮演的角色，我的观点十分明确，那就是：NHST不应该在统计推断或科学发现推论过程中有立足之地。因为在NHST的推断/推理框架下连续型的统计检验量（如p-值，置信区间，贝叶斯因子）被人为地两分化/离散化以达到划分‘统计显著性’的目的并以此作为评判科学发现假设真伪（拒绝/接受）的标准。这个统计推断范式在逻辑上站不住脚，在技术层面上漏洞百出，在实践上造成了科研工作者忽视科学内涵/相关学科机理、盲目接受统计检验结果的严重后果。我们应该谦虚地承认，在只有一组样本数据的条件下统计抽样分布根本无从谈起，统计数据分析充其量能做到的只是给出“在给定假设条件下的数据模型”分析结果(what-if analysis)，而不是所谓的能确认科学发现假设真伪的分析(confirmatory analysis)。这样的结论是在我收集、阅读、思考及消化了尽可能多的相关参考资料后逐渐形成的。

以下是一份截至2020年底我所收集的有关NHST这个对统计学理论与实践都十分重要的话题的不完整的参考资料清单（按时间顺序排列）。公平起见，我已尽量把正反两方的资料都收集，但我能查到的大部分的都是指出NHST种种问题的资料。我给对统计假设检验问题有兴趣的博友的阅读建议是：如果您只有时间读一篇文章，请选择下述资料清单的第146项；如果您有时间读两篇文章，请考虑下述资料清单的第141 和146项；如果您有时间读一本书的话，我强力推荐资料清单的第2项 - (The Lady Tasting Tea) – 中国统计出版社译为“女士品茶”（链接https://www.bookresource.net/pdf/151336.html）；如果您有半个小时，我建议您看看Geoff Cumming教授的这段关于p-值话题的精彩视频https://www.youtube.com/watch?v=iJ4kqk3V8jQ （资料清单的第152项）。我相信您一定会觉得花一点时间关注一下这个对统计学理论与实践都十分重要的话题实在是值得的。

第一部分：参考书籍（Part I: Books）

1.       Edited by Denton E. Morrison and Ramon E. Henkel (1970). The Significance Test Controversy. Routledge, Taylor & Francis Group.

2.       David Salsburg (2001). The Lady Tasting Tea: How Statistics Revolutionized Science in the Twentieth Century. Henry Holt and Company.

3.       Burham, K. and Anderson, D. (2002). Model Selection and Multimodel Inference: a practical information-theoretic approach.  Springer.

4.       E.T. Jaynes (edited by G. Larry Bretthorst) (2003). Probability Theory: the logic of science. Cambridge University Press.

5.       Richard A. Berk (2004). Regression Analysis: A Constructive Critique. SAGE.

6.       Stephen T. Ziliak and Deirdre N. McCloskey (2008). The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives.  The University of Michigan Press.

7.       Raymond Hubbard (2015).  Corrupt Research: The case for reconceptualizing empirical management and social science.  SAGE Publications, Inc.

8.       Richard McElreath (2016). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press, Taylor & Francis Group.

9.       Weichung Joe Shih and Joseph Aisner (2016).  Statistical Design and Analysis of Clinical Trials: Principles and Methods.  CRC Press, Taylor & Francis Group.

10.   Geoff Cumming and Robert Calin-Jageman (2017). Introduction to The New Statistics: Estimation, Open Science, & Beyond.  Routledge.

11.   Hadley Wickham & Garrett Grolemund (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data.  O’Reilly. 

12.   Richard F. Harris (2017).  Rigor Mortis: How Sloppy Science Creates Worthless Cures, Crushes Hope, and Wastes Billions.  BASIC BOOKS.

13.   Edited by Vladik Kreinovich, Nguyen Ngoc Thach, Nguyen Duc Trung, and Dang Van Thanh (2019). Beyond Traditional Probabilistic Methods in Economics.  Springer

14.   David Spiegelhalter (2019).  The Art of Statistics: How to learn from data.  BASIC BOOKS, New York. 

第二部分：期刊文章（或参考书的章节）（Part II: Articles (including book chapters if any)）

15.   F. Yates (1951). The Influence of Statistical Methods for Research Workers on the Development of the Science of Statistics. Journal of the American Statistical Association, Vol. 46, No. 253, pp. 19-34

16.   William W. Rozeboom (1960). THE FALLACY OF THE NULL-HYPOTHESIS SIGNIFICANCE TEST. Psychological Bulletin, Vol. 57, No. S, 416-428

17.   David Bakan (1966). The Test of Significance in Psychological Research. Psychological Bulletin, Vol. 66, No. 6, 423-437.

18.   Ronald N. Giere (1972). The Significance Test Controversy. The British Journal for the Philosophy of Science, Vol. 23, No. 2, pp. 170-181.

19.   W. Edwards Deming (1975).  On Probability As a Basis For Action.  The American Statistician, Vol. 29, No. 4, pp. 146-152.

20.   George E. P. Box (1976).  Science and Statistics.  Journal of the American Statistical Association, Vol. 71, No. 356, pp. 791-799.

21.   Leonard J. Savage (1976).  On Rereading R. A. Fisher.  The annals of Statistics, Vol. 4, No. 3, 441-500.

22.   Ronald P. Carver (1978).  The Case Against Statistical Significance Testing.  Harvard Educational Review, Vol. 48, Issue 3, pages 378-399.

23.   Mario Bunge (1981). Four concepts of probability. Appl. Math. Modelling, Vol. 5, pp. 306-312.

24.   C. Chatfield (1985). The Initial Examination of Data.  Journal of the Royal Statistical Society. Series A (General), Vol. 148, No. 3, pp. 214-253.

25.   Terry Speed (1986).  Questions, Answers and Statistics.  ICOTS 2, 18-28.

26.   Martin J. Gardner and Douglas G. Altman (1986).  Confidence intervals rather than P values:  estimation rather than hypothesis testing.  Statistics in Medicine, British Medical Journal, Vol. 292, pp. 746-750.

27.   James O. Berger and Thomas Sellke (1987). Testing a Point Null Hypothesis: The Irreconcilability of P Values and Evidence. Journal of the American Statistical Association, Vol. 82, No. 397, pp.112-122.

28.   Steven N. Goodman & Richard Royall (1988). Evidence and Scientific Research. (Commentary) American Journal of Public Health, Vol. 78, No. 12, pp. 1568-1574.

29.   Nigel G. Yoccoz (1991). Use, Overuse, and Misuse of Significance Tests in Evolutionary Biology and Ecology.  Bulletin of the Ecological Society of America, Vol. 72, No. 2, pp. 106-111.

30.   E.L. Lehmann (1993). The Fisher, Neyman-Peerson Theories of Testing Hypotheses: One Theory or Two?  Journal of the American Statistical Association, Vol. 88, No. 424, 201-208.

31.   Gerald J. Hahn and William Q. Meeker (1993).  Assumptions for Statistical Inference.  The American Statistician, Vol. 47, No. 1, pp. 1-11

32.   Ronald P. Carver (1993).  The Case Against Statistical Significance Testing, Revisited.  Journal of Experimental Education, 61(A), 287-292.

33.   Rama Menon (1993). Statistical Significance Testing Should be Discontinued in Mathematics Education Research. Mathematics Education Research Journal, Vol. 5, No. 1, 4-18.

34.   Steven N. Goodman (1993). p Values, Hypothesis Tests, and Likelihood: Implications for Epidemiology of a Neglected Historical Debate. American Journal of Epidemiology, Vol. 137, No.5, pp.485-496.

35.   Jacob Cohen (1994).  The Earth Is Round (p < .05). American Psychologist, Vol.49, No. 12, 997-1003.

36.   Bruce Thompson (1994). The Concept of Statistical Significance Testing.  Practical Assessment, Research & Evaluation, Vol. 4, No. 5, Available online: http://PAREonline.net/getvn.asp?v=4&n=5.

37.   Bruce Thompson (1994).  The Pivotal Role of Replication in Psychological Research:  Empirically Evaluating the Replicability of Sample Results.  Journal of Personality 62:2, 157-176.

38.   Ruma Falk & Charles W. Greenbaum (1995).  Significance Test Die Hard: The Amazing Persistence of a Probabilistic Misconception.  Theory & Psychology 5(1), 75-98.

39.   R.E. Kirk (1996). Practical significance: A concept whose time has come. Educational and Psychological Measurement, 56, 746-759.

40.   Marks R. Nester (1996). An Applied Statistician’s Creed. Journal of the Royal Statistical Society.  Series C (Applied Statistics), Vol. 45, No. 4, 401-410.

41.   Frank L. Schmidt (1996). Statistical Significance Testing and Cumulative Knowledge in Psychology: Implications for Training of Researchers.  Psychological Methods, Vol. 1, No. 2, 115-129.

42.   Bruce Thompson (1996). AERA Editorial Policies Regarding Statistical Significance Testing: Three Suggested Reforms. Educational Researcher, Vol. 25, No. 2, pp. 26-30.

43.   Robert P. Abelson (1997). On the Surprising Longevity of Flogged Horses: Why There Is a Case for the Significance Test.  Psychological Science, Volume 8 issue 1, pp. 12-15.

44.   Patrick E. Shrout (1997). Should Significance Tests Be Banned? Introduction to a Special Section Exploring the Pros and Cons.  Psychological Sciences, Vol. 8, No. 1, 1-2.

45.   Frank L. Schmidt (1997). Eight common but false objections to the discontinuation of significance testing in the analysis of research data, in book: What if there were no significance tests?  Editors: Lisa L. Harlow, Stanley A. Mulaik, James H.Steiger, Publisher: Lawrence Erlbaum Associates.

46.   Janet M. Lang, Kenneth J. Rothman, and Cristina I. Cann (1998). That Confounded P-Value. Epidemiology, Volume 9, Number 1, 7-8. 

47.   James E. McLean and James M. Ernest (1998). The Role of Statistical Significance Testing In Educational Research. Research in the Schools, Vol. 5, No. 2, 15-22.

48.   James Currall (1998). Review on the book ‘Statistical Significance: Rationale, Validity and Utility’ (Siu L. CHOW, 1996). Journal of the Royal Statistical Society. Series D (The Statistician), Vol. 47, No. 2, pp. 394-395

49.   Robert W. Frick; Gerd Gigerenzer (1998).  Two individual reviews on the book ‘Statistical Significance: Rationale, Validity and Utility’ (Siu L. CHOW, 1996). BEHAVIORAL AND BRAIN SCIENCES (1998) 21:2, 199-200.

50.   Tapan K. Nayak (1998). Review on the book ‘Statistical Significance: Rationale, Validity and Utility’ (Siu L. CHOW, 1996). TECHNOMETRICS, MAY 1998, VOL. 40, NO. 2

51.   David H. Krantz (1999). The Null Hypothesis Testing Controversy in Psychology. Journal of the American Statistical Association, Vol. 44, No. 448, pp. 1372-1381.

52.   Douglas H. Johnson (1999). The Insignificance of Statistical Significance Testing.  Journal of Wildlife Management, 63(3): 763-772.

53.   Howard Wainer (1999).  One Cheer for Null Hypothesis Significance Testing.  Psychological Methods, Vol. 4, No. 2, 212-213.

54.   Anderson, D. R., Burnham, K. P., and Thompson, W. L. (2000). Null Hypothesis Testing: Problems, Prevalence, and an Alternative. Journal of Wildlife Management, 64, 912–923.

55.   John I. Marden (2000).  Hypothesis Testing: From p Values to Bayes Factors.  Journal of the American Statistical Association, Vol. 95, No. 452, 1316-1320.

56.   Raymond S. Nickerson (2000).  Null Hypothesis Significance Testing: A Review of an Old and Continuing Controversy.  Psychological Methods, Vol. 5, No. 2, 241-301.

57.   Charles Poole (2001). Low P-values or Narrow Confidence Intervals: Which Are More Durable? Epidemiology, Vol. 12, No. 3, 291-294.

58.   Joachim Krueger (2001).  Null Hypothesis Significance Testing: On the Survival of a Flawed Method.  American Psychologist, Vol. 56, No. 1, 16-26.  DOI: 10.1037//0003-066X.56.1.16.

59.   Jonathan A. C. Sterne and George Davey Smith (2001).  Sifting the evidence-what’s wrong with significance tests?  BMJ, 322:226-31.

60.   Gerd Gigerenzer (2002). The Superego, the Ego, and the Id in Statistical Reasoning. Print publication date: 2002; Published to Oxford Scholarship Online: October 2011; DOI: 10.1093/acprof:oso/9780195153729.001.0001.

61.   Jeffrey A. Gliner, Nancy L. Leech, and George A. Morgan (2002). Problems With Null Hypothesis Significance Testing (NHST): What Do the Textbooks Say? The Journal of Experimental Education, 71(1), 83-92.

62.   Shlomo S. Sawilowsky (2003). Deconstructing arguments from the case against hypothesis testing. Journal of Modern Applied Statistical Methods, 2(2), 467-474. Available at: http://digitalcommons.wayne.edu/coe_tbf/17

63.   Michael D. Jennions and Anders Pape Moller (2003).   A survey of the statistical power of research in behavioral ecology and animal behaviour.  Behavioral Ecology Vol. 14 No. 3: 438–445.

64.   Raymond Hubbard, M. J. Bayarri, Kenneth N. Berk and Matthew A. CarltonSource (2003). Confusion over Measures of Evidence (p's) versus Errors (α's) in Classical Statistical Testing. The American Statistician, Vol. 57, No. 3, pp. 171-182.

65.   Shinichi Nakagawa (2004). A farewell to Bonferroni: the problems of low statistical power and publication bias.  Behavioural Ecology, Vol. 15, No. 6: 1044-1045, doi:10.1093/beheco/arh107.

66.   Gerd Gigerenzer (2004).  Mindless statistics.  The Journal of Socio-Economics 33, 587–606.

67.   Ioannidis JPA (2005). Why most published research findings are false. PLoS Med 2: e124. doi:10.1371/journal.pmed.0020124

68.   Nekane Balluerka, Juana Gomez, and Dolores Hidalgo (2005).  The Controversy over Null Hypothesis Significance Testing Revisited.  Methodology European Journal of Research Methods for the Behavioral and Social Sciences, Vol. 1(2):55–70, DOI 10.1027/1614-1881.1.2.55

69.   Editorial (2006). Some experimental design and statistical criteria for analysis of studies in manuscripts submitted for consideration for publication.  Animal Feed Science and Technology 129, 1-11.

70.   Andrew Gelman and Hal Stern (2006).  The Difference Between “Significant” and “Not Significant” is not Itself Statistically Significant.  The American Statistician, November 2006, Vol. 60, No. 4, 328-331.

71.   Stephen Gorard (2006). Towards a judgement-based statistical analysis.  British Journal of Sociology of Education, 27:1, 67-80, DOI: 10.1080/01425690500376663

72.   Goodman S, Greenland S (2007). Why most published research findings are false: Problems in the analysis. PLoS Med 4(4): e168. doi:10.1371/journal.pmed.0040168

73.   Raymond Hubbard and J. Scott Armstrong (2006). Why We Don't Really Know What Statistical Significance Means: A Major Educational Failure. Journal of Marketing Education, Vol. 28, pp. 114-120.

74.   James M. Gibbons, Neil M.J. Crout and John R. Healey (2007). What role should null-hypothesis significance tests have in statistical education and hypothesis falsification? (Letter to editor) TRENDS in Ecology and Evolution Vol.22 No.9, 445-446.

75.   Shinichi Nakagawa and Innes C. Cuthill (2007). Effect size, confidence interval and statistical significance: a practical guide for biologists.  Biol. Rev., 82, pp. 591-605, doi:10.1111/j.1469-185X.2007.00027.x.

76.   Zab Mosenifar (2007).  Population Issues in Clinical Trials.  Proc Am Thorac Soc Vol 4. pp 185–188, DOI: 10.1513/pats.200701-009GC.

77.   Timothy R. Levine, et al. (2008). A Critical Assessment of Null Hypothesis Significance Testing in Quantitative Communication Research.  Human Communication Research 34, 171–187.

78.   Aris Spanos (2008). Review of Stephen T. Ziliak and Deirdre N. McCloskey’s The cult of statistical significance: how the standard error costs us jobs, justice, and lives. Ann Arbor (MI): The University of Michigan Press, 2008, xxiii+322 pp. Erasmus Journal for Philosophy and Economics, Volume 1, Issue 1, pp. 154-164.

79.   Stephen T. Ziliak and Deirdre N. McCloskey (2008). Science is judgment, not only calculation: a reply to Aris Spanos’s review of The cult of statistical significance. Erasmus Journal for Philosophy and Economics, Volume 1, Issue 1, pp. 165-170.

80.   Timothy R. Levine, Rene Weber, Craig Hullett, Hee Sun Park, & Lisa L. Massi Lindsey (2008).  A Critical Assessment of Null Hypothesis Significance Testing in Quantitative Communication Research. Human Communication Research 34, pp. 171–187. doi:10.1111/j.1468-2958.2008.00317.x.

81.   Stuart H. Hurlbert and Celia M. Lombardi (2009).  Final Collapse of the Neyman-Pearson decision theoretic framework and rise of the neoFisherian.  Ann. Zool. Fennici 46: 311-349.

82.   Stephen R. Cole and Elizabeth A. Stuart (2010).  Generalizing Evidence From Randomized Clinical Trials to Target Populations The ACTG 320 Trial.  American Journal of Epidemiology, 172:107–115.

83.   Joseph Lee Rodgers (2010). The Epistemology of Mathematical and Statistical Modeling: A Quiet Methodological Revolution. American Psychologist, Vol. 65, No. 1, 1–12. DOI: 10.1037/a0018326.

84.   Stephen Gorard (2010).  All evidence is equal: the flaw in statistical reasoning.  Oxford Review of Education, Vol. 36, No. 1, pp. 63-77.

85.   Andreas Stang, Charles Poole, and Oliver Kuss (2010).  The ongoing tyranny of statistical significance testing in biomedical research.  Eur J Epidemiol 25:225-230.  DOI 10.1007/s10654-010-9440-x

86.   Daniel Greco (2011). Significance Testing in Theory and Practice. Brit. J. Phil. Sci. 62, 607–637. doi:10.1093/bjps/axq023.

87.   Douglas G. Altman (2011).  How to obtain the P value from a confidence interval. BMJ, 343:d2304, doi: https://doi.org/10.1136/bmj.d2304 

88.   James Tabery (2011). Commentary: Hogben vs the Tyranny of Averages. International Journal of Epidemiology, 40:1458–1460, doi:10.1093/ije/dyr031

89.   John P. A. Ioannidis (2012). Why Science Is Not Necessarily Self-Correcting.  Perspectives on Psychological Science 7(6) 645-654. DOI: 10.1177/1745691612464056.

90.   Andrew Gelman (2013). P Values and Statistical Practice. Epidemiology, Volume 24, Number 1, 69-72.

91.   Jesper W. Schneider (2013).  Caveats for using statistical significance tests in research assessments. Journal of Informetrics 7, 50– 62.

92.   Andreas Stang and Charles Poole (2013). The researcher and the consultant: a dialogue on null hypothesis significance testing.  Eur J Epidemiol (2013) 28:939–944, DOI 10.1007/s10654-013-9861-4

93.   Dalson Britto Figueiredo Filho, et al. (2013).  When is statistical significance not significant?  Brazilianpoliticalsciencereview, 7(1), pages 31-55.

94.   Andrew Gelman and Eric Loken (2014).  The Statistical Crisis in Science: Data-dependent analysis – a “garden of forking paths” – explains why many statistically significant comparisons don’t hold up.  American Scientist, Volume 102, pp. 460-465.

95.   Andrew Gelman and John Carlin (2014).  Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors.  Perspectives on Psychological Science, Vol. 9(6) 641-651.

96.   Regina Nuzzo (2014).  Statistical Errors: p values, the ‘gold standard’ of statistical validity, are not as reliable as many scientists assume.  Nature, Vol. 506, 150-152.

97.   Geoff Cumming (2014).  The New Statistics: Why and How.  Psychological Science, Vol. 25(1), 7-29, DOI: 10.1177/0956797613504966

98.   Gerd Gigerenzer & Julian N. Marewski (2014). Surrogate Science: The Idol of a Universal Method for Scientific Inference. Journal of Management, Vol. 41, No. 2, pp. 421-440. DOI: 10.1177/0149206314547522.

99.   Paul A. Murtaugh (2014).  In defense of P values.  Ecology, 95(3), 2014, pp. 611–617.

100.                       S. Gorard (2014).  The widespread abuse of statistics by researchers: what is the problem and what is the ethical way forward?  Psychology of education review, 38 (1). pp. 3-10.

101.                       P. White (2014).  A Response to Gorard: The widespread abuse of statistics by researchers: What is the problem and what is the ethical way forward? The Psychology of Education Review, 38(1), pp. 24-28.

102.                       Editorial (2014).  Business Not as Usual.  Psychological Science, Vol. 25(1) 3-6. DOI: 10.1177/0956797613512465.

103.                       Dave Neale (2015). Defending the logic of significance testing: a response to Gorard. Oxford Review of Education, 41:3, 334-345, DOI: 10.1080/03054985.2015.1028526

104.                       Jesper W. Schneider (2015).  Null hypothesis significance tests. A mix-up of two different theories: the basis for widespread confusion and numerous misinterpretations.  Scientometrics, 102: 411-432, DOI 10.1007/s11192-014-1251-5.

105.                       Gerd Gigerenzer & Julian N. Marewski (2015). Surrogate Science: The Idol of a Universal

Method for Scientific Inference. Journal of Management, Vol. 41 No. 2, February 2015 421–440, DOI: 10.1177/0149206314547522.

106.                       Jose D. Perezgonzalez (2015). Fisher, Neyman-Pearson or NHST? A tutorial for teaching data testing.  Frontiers in Psychology, Volume 6, Article 223.

107.                       Roger Peng (2015) The reproducibility crisis in science: A statistical counterattack. Science: significance, pp.30-32. The Royal Statistical Society.

108.                       Ronald L. Wasserstein & Nicole A. Lazar (2016). The ASA's Statement on p-Values: Context, Process, and Purpose. The American Statistician, 70:2, 129-133, DOI:10.1080/00031305.2016.1154108.

109.                       John Concato & John A. Hartigan (2016). P values: from suggestion to superstition. J Investig Med 2016;64:1166–1171. doi:10.1136/jim-2016-000206

110.                       Blakeley B. McShane and David Gal (2016).  Blinding Us to the Obvious? The Effect of Statistical Training on the Evaluation of Evidence. Management Science 62(6):1707-1718. http://dx.doi.org/10.1287/mnsc.2015.2212

111.                       Kenneth J. Rothman (2016).  Disengaging from statistical significance.  Eur J Epidemiol (2016) 31:443–444. DOI 10.1007/s10654-016-0158-2

112.                       Sander Greenland, et al. (2016).  Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations.  Eur J Epidemiol (2016) 31:337–350, DOI 10.1007/s10654-016-0149-3

113.                       Andrew Gelman (2016). The Problems With P-Values are not Just With P-Values. Online discussion of the ASA Statement on Statistical Significance and P-Values, The American Statistician, 70.

114.                       Jeehyoung Kim and Heejung Bang (2016).  Three common misuses of P values.  Dent Hypotheses, 7(3): 73–80. doi:10.4103/2155-8213.190481

115.                       Kenneth J. Rothman (2016).  Disengaging from statistical significance.  Eur J Epidemiol 31:443–444 DOI 10.1007/s10654-016-0158-2

116.                       Robert E. Kass, Brian S. Caffo, Marie Davidian, Xiao-Li Meng, Bin Yu and Nancy Reid (2016). Ten Simple Rules for Effective Statistical Practice. (Editorial) PLOS Computational Biology | DOI:10.1371/journal.pcbi.1004961.

117.                       Steven N. Goodman, Daniele Fanelli, John P. A. Ioannidis (2016). What does research reproducibility mean?  Sci Transl Med 8, 341ps12341ps12, DOI: 10.1126/scitranslmed.aaf5027

118.                       Amrhein et al. (2017). The earth is flat (p > 0:05): significance thresholds and the crisis of unreplicable research. PeerJ 5:e3544; DOI 10.7717/peerj.3544

119.                       Donald Berry (2017).  A p-Value to Die For.  Journal of the American Statistical Association, 112:519, 895-897, DOI: 10.1080/01621459.2017.1316279

120.                       Robert Matthews (2017).  The ASA’s p-value statement, one year on.  The Royal Statistical Society, In Practice, 38-41.

121.                       Joseph Kang, Jaeyoung Hong, Precious Esie, Kyle T. Bernstein, and Sevgi Aral (2017).  An Illustration of Errors in Using the P Value to Indicate Clinical Significance or Epidemiological Importance of a Study Finding.  Sex Transm Dis., 44(8): 495–497. doi:10.1097/OLQ.0000000000000635

122.                       Brian D. Haig (2017).  Tests of Statistical Significance Made Sound.  Educational and Psychological Measurement, Vol. 77(3) 489–506

123.                       Denes Szucs and John P.A. Ioannidis (2017). When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment. Frontiers in Human Neuroscience, Volume 11, Article 390.

124.                       Timothy L. Lash (2017).  The Harm Done to Reproducibility by the Culture of Null Hypothesis Significance Testing.  American Journal of Epidemiology, Vol. 186, No. 6, DOI: 10.1093/aje/kwx261

125.                       Sander Greenland (2017).  Invited Commentary: The Need for Cognitive Science in Methodology.  American Journal of Epidemiology, Vol. 186, No. 6

DOI: 10.1093/aje/kwx259

126.                       Andrew Gelman (2018).  The Failure of Null Hypothesis Significance Testing When Studying Incremental Changes, and What to Do About It.  Personality and Social Psychology Bulletin, Vol. 44(1) 16-23.

127.                       Benjamin et al. (2018). Redefine Statistical Significance. Nature Human

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128.                       Jeffrey R. Spence and David J. Stanley (2018). Concise, Simple, and Not Wrong: In Search of a Short-Hand Interpretation of Statistical Significance.  Frontiers in Psychology, Volume 9, Article 2185.

129.                       Harry Crane (2018). The Impact of P-hacking on “Redefine Statistical Significance”. Basic and Applied Social Psychology, 40:4, 219-235, DOI: 10.1080/01973533.2018.1474111.

130.                       Gerd Gigerenzer (2018). Statistical Rituals: The Replication Delusion and How We Got There. Advances in Methods and  Practices in Psychological Science, Vol. 1(2) 198 –218.

131.                       Van Calster B, Steyerberg, EW, Collins GS, and Smits T. (2018). Consequences of relying on statistical significance: Some illustrations. Eur J Clin Invest. 48:e12912. https://doi.org/10.1111/eci.12912 .

132.                       Valentin Amrhein, Sander Greenland, Blake McShane (2019). Retire statistical significance. Nature, Vol. 567, 305: Comment.

133.                       Ronald D. Fricker Jr., Katherine Burke, Xiaoyan Han & William H. Woodall (2019).  Assessing the Statistical Analyses Used in Basic and Applied Social Psychology After Their p-Value Ban. The American Statistician, 73:sup1, 374-384, DOI: 10.1080/00031305.2018.1537892

134.                       Blakeley B. McShane, et al. (2019). Abandon Statistical Significance. The American Statistician, Vol. 73, No. S1, 235-245: Statistical Inference in the 21st Century.

135.                       Christopher Tong (2019).  Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science. The American Statistician, Vol. 73, No. S1, 246-261: Statistical Inference in the 21st Century.

136.                       Dana P. Turner, Hao Deng and Timothy T. Houle (Guest Editorial, 2019). Statistical Hypothesis Testing: Overview and Application. Headache, pages 302-307. doi: 10.1111/head.13706.

137.                       Deborah G. Mayo (2019). P‐value thresholds: Forfeit at your peril. Eur J Clin Invest., 49:e13170. https://doi.org/10.1111/eci.13170

138.                       Andrew Gelman (2019). When we make recommendations for scientific practice, we are (at best) acting as social scientists. Eur J Clin Invest., 49:e13165. DOI: 10.1111/eci.13165

139.                       Tom E. Hardwicke & John P.A. Ioannidis (2019). Petitions in scientific argumentation: Dissecting the request to retire statistical significance. Eur J Clin Invest., 49:e13162. https://doi.org/10.1111/eci.13162

140.                       Horbert Hirschauer, Sven Gruner, oliver Muβhoff and Claudia Becker (2019).  Twenty Steps Towards an Adequate Inferential Interpretation of p-Values in Econometrics. Journal of Economics and Statistics, 239(4):703–721

141.                       Raymond Hubbard, Brian D. Haig & Rahul A. Parsa (2019). The Limited Role of

Formal Statistical Inference in Scientific Inference. The American Statistician, 73:sup1, 91-98, DOI: 10.1080/00031305.2018.1464947

142.                       Raymond Hubbard (2019).  Will the ASA's Efforts to Improve Statistical Practice be Successful? Some Evidence to the Contrary.  The American Statistician, 73:sup1, 31-35, DOI:

10.1080/00031305.2018.1497540

143.                       Rob Herbert (2019).  Research Note: Significance testing and hypothesis testing: meaningless, misleading and mostly unnecessary.  Journal of Physiotherapy, 65, 178-181.

144.                       Valentin Amrhein, David Trafimow & Sander Greenland (2019). Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication.  The American Statistician, Vol. 73, No. S1, 262-270: Statistical Inference in the 21st Century.

145.                       Vincent S. Staggs (2019). Why statisticians are abandoning statistical significance. Guest Editorial, Res Nurs Health, 42:159–160, DOI: 10.1002/nur.21947.

146.                       Ronald L. Wasserstein, Allen L. Schirm & Nicole A. Lazar (2019).  Moving to a World Beyond “p<0.05”. The American Statistician, Vol. 73, No. S1, 1-19: Editorial.

147.                       Caiyun Liao, Andrew L. Speirs, Sierra Goldsmith, & Sherman J. Silber (2020).  When “facts” are not facts: what does p value really mean, and how does it deceive us?  Journal of Assisted Reproduction and Genetics, 37:1303-1310, https://doi.org/10.1007/s10815-020-01751-4.

第三部分：网上资料（Part III:  Online materials）

148.                       http://www.stats.org.uk/statistical-inference/  the link for Statistical Inference (and What is Wrong With Classical Statistics) – a long list of references.

149.                       https://fionaresearch.files.wordpress.com/2013/06/fidler-phd-2006.pdf Fiona Fidler’s PhD thesis “FROM STATISTICAL SIGNIFICANCE TO EFFECT ESTIMATION: STATISTICAL REFORM IN PSYCHOLOGY, MEDICINE AND ECOLOGY.”

150.                       https://learningstatisticswithr.com/book/ Learning statistics with R: A tutorial for paychology students and other beginners (Version 0.6.1).  2019-01-11, Danielle Navarro (UNSW, Australia)

151.                       https://www.fharrell.com/post/introduction/ Frank Harrell, author of an influential book on regression modeling and currently both a biostatistics professor and a statistician at the Food and Drug Administration sums up “some of his personal philosophy of statistics” here.

152.                       https://www.youtube.com/watch?v=iJ4kqk3V8jQ  online video presented by Professor Geoff Cumming, La Trobe University, Australia

(注：上述NHST问题参考资料清单最早发表在我的researchgate的个人网页上https://www.researchgate.net/project/Say-NO-to-Null-Hypothesis-Significance-Testing )