1. Peng R. The reproducibility crisis in science: a statistical counterattack. Significance 2015;12:30–32.

1. Peng R. The reproducibility crisis in science: a statistical counterattack. Significance 2015;12:30–32.)| false
• Search Google Scholar
• Export Citation

2. Barba LA. The hard road to reproducibility. Science 2016;354:142.

2. Barba LA. The hard road to reproducibility. Science 2016;354:142.)| false
• Search Google Scholar
• Export Citation

3. Munafo MR, Nosek BA, Bishop DVM, et al. A manifesto for reproducible science. Nat Hum Behav 2017;1:0021.

3. Munafo MRNosek BABishop DVM, et al. A manifesto for reproducible science. Nat Hum Behav 2017;1:0021.)| false
• Search Google Scholar
• Export Citation

4. Baker M. 1,500 scientists lift the lid on reproducibility. Nature 2016;533:452–454.

4. Baker M. 1,500 scientists lift the lid on reproducibility. Nature 2016;533:452–454.)| false
• Search Google Scholar
• Export Citation

5. Collins FS, Tabak LA. Policy: NIH plans to enhance reproducibility. Nature 2014;505:612–613.

5. Collins FSTabak LA. Policy: NIH plans to enhance reproducibility. Nature 2014;505:612–613.)| false
• Search Google Scholar
• Export Citation

6. Begley CG. Six red flags for suspect work. Nature 2013; 497:433–434.

6. Begley CG. Six red flags for suspect work. Nature 2013; 497:433–434.)| false
• Search Google Scholar
• Export Citation

7. Perrin S. Preclinical research: make mouse studies work. Nature 2014;507:423–425.

7. Perrin S. Preclinical research: make mouse studies work. Nature 2014;507:423–425.)| false
• Search Google Scholar
• Export Citation

8. Begley CG, Ellis LM. Drug development: raise standards for preclinical cancer research. Nature 2012;483:531–533.

8. Begley CGEllis LM. Drug development: raise standards for preclinical cancer research. Nature 2012;483:531–533.)| false
• Search Google Scholar
• Export Citation

9. Begley CG, Ioannidis JP. Reproducibility in science: improving the standard for basic and preclinical research. Circ Res 2015;116:116–126.

9. Begley CGIoannidis JP. Reproducibility in science: improving the standard for basic and preclinical research. Circ Res 2015;116:116–126.)| false
• Search Google Scholar
• Export Citation

10. Ioannidis JP. Why most published research findings are false. PLoS Med 2005;2:e124.

10. Ioannidis JP. Why most published research findings are false. PLoS Med 2005;2:e124.)| false
• Search Google Scholar
• Export Citation

11. Nuzzo R. Scientific method: statistical errors. Nature 2014;506:150–152.

11. Nuzzo R. Scientific method: statistical errors. Nature 2014;506:150–152.)| false
• Search Google Scholar
• Export Citation

12. Greenland S. Multiple comparisons and association selection in general epidemiology. Int J Epidemiol 2008;37:430–434.

12. Greenland S. Multiple comparisons and association selection in general epidemiology. Int J Epidemiol 2008;37:430–434.)| false
• Search Google Scholar
• Export Citation

13. Guller U, DeLong ER. Interpreting statistics in medical literature: a vade mecum for surgeons. J Am Coll Surg 2004;198:441–458.

13. Guller UDeLong ER. Interpreting statistics in medical literature: a vade mecum for surgeons. J Am Coll Surg 2004;198:441–458.)| false
• Search Google Scholar
• Export Citation

14. Head ML, Holman L, Lanfear R, et al. The extent and consequences of p-hacking in science. PLoS Biol 2015;13:e1002106.

14. Head MLHolman LLanfear R, et al. The extent and consequences of p-hacking in science. PLoS Biol 2015;13:e1002106.)| false
• Search Google Scholar
• Export Citation

15. Bender R, Lange S. Adjusting for multiple testing—when and how? J Clin Epidemiol 2001;54:343–349.

15. Bender RLange S. Adjusting for multiple testing—when and how? J Clin Epidemiol 2001;54:343–349.)| false
• Search Google Scholar
• Export Citation

16. Kerr NL. HARKing: hypothesizing after the results are known. Pers Soc Psychol Rev 1998;2:196–217.

16. Kerr NL. HARKing: hypothesizing after the results are known. Pers Soc Psychol Rev 1998;2:196–217.)| false
• Search Google Scholar
• Export Citation

17. Delgado-Rodríguez M, Llorca J. Bias. J Epidemiol Community Health 2004;58:635–641.

17. Delgado-Rodríguez MLlorca J. Bias. J Epidemiol Community Health 2004;58:635–641.)| false
• Search Google Scholar
• Export Citation

18. Lanyon L. Evidence-based veterinary medicine: a clear and present challenge. Vet Rec 2014;174:173–175.

18. Lanyon L. Evidence-based veterinary medicine: a clear and present challenge. Vet Rec 2014;174:173–175.)| false
• Search Google Scholar
• Export Citation

19. Vandeweerd JM, Kirschvink N, Clegg P, et al. Is evidence-based medicine so evident in veterinary research and practice? History, obstacles and perspectives. Vet J 2012;191:28–34.

19. Vandeweerd JMKirschvink NClegg P, et al. Is evidence-based medicine so evident in veterinary research and practice? History, obstacles and perspectives. Vet J 2012;191:28–34.)| false
• Search Google Scholar
• Export Citation

20. White BJ, Larson RL. Systematic evaluation of scientific research for clinical relevance and control of bias to improve clinical decision making. J Am Vet Med Assoc 2015;247:496–500.

20. White BJLarson RL. Systematic evaluation of scientific research for clinical relevance and control of bias to improve clinical decision making. J Am Vet Med Assoc 2015;247:496–500.)| false
• Search Google Scholar
• Export Citation

21. Kelsey JL. A contrary view on statistical significance. J Am Vet Med Assoc 2011;239:428–429.

21. Kelsey JL. A contrary view on statistical significance. J Am Vet Med Assoc 2011;239:428–429.)| false
• Search Google Scholar
• Export Citation

22. West CP, Dupras DM. 5 ways statistics can fool you. Tips for practicing clinicians. Vaccine 2013;31:1550–1552.

22. West CPDupras DM. 5 ways statistics can fool you. Tips for practicing clinicians. Vaccine 2013;31:1550–1552.)| false
• Search Google Scholar
• Export Citation

23. Mullin CM, Arkans MA, Sammarco CD, et al. Doxorubicin chemotherapy for presumptive cardiac hemangiosarcoma in dogs. Vet Comp Oncol 2016;14:e171–e183.

23. Mullin CMArkans MASammarco CD, et al. Doxorubicin chemotherapy for presumptive cardiac hemangiosarcoma in dogs. Vet Comp Oncol 2016;14:e171–e183.)| false
• Search Google Scholar
• Export Citation

24. Holtermann N, Kiupel M, Kessler M, et al. Masitinib monotherapy in canine epitheliotropic lymphoma. Vet Comp Oncol 2016;14(suppl 1):127–135.

24. Holtermann NKiupel MKessler M, et al. Masitinib monotherapy in canine epitheliotropic lymphoma. Vet Comp Oncol 2016;14(suppl 1):127–135.)| false
• Search Google Scholar
• Export Citation

25. Lehmann EL. The Fisher, Neyman-Pearson theories of testing hypotheses: one theory or two? J Am Stat Assoc 1993;88:1242–1249.

25. Lehmann EL. The Fisher, Neyman-Pearson theories of testing hypotheses: one theory or two? J Am Stat Assoc 1993;88:1242–1249.)| false
• Search Google Scholar
• Export Citation

26. Sterne JA, Davey Smith G. Sifting the evidence-what's wrong with significance tests? BMJ 2001;322:226–231.

26. Sterne JADavey Smith G. Sifting the evidence-what's wrong with significance tests? BMJ 2001;322:226–231.)| false
• Search Google Scholar
• Export Citation

27. Jeffery N. Liberating the (data) population from subjugation to the 5% (P-value). J Small Anim Pract 2015;56:483–484.

27. Jeffery N. Liberating the (data) population from subjugation to the 5% (P-value). J Small Anim Pract 2015;56:483–484.)| false
• Search Google Scholar
• Export Citation

28. McShane B, Gal D, Gelman A, et al. Abandon statistical significance. Am Stat 2019;73:235–245.

28. McShane BGal DGelman A, et al. Abandon statistical significance. Am Stat 2019;73:235–245.)| false
• Search Google Scholar
• Export Citation

29. Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature 2019;567:305–307.

29. Amrhein VGreenland SMcShane B. Scientists rise up against statistical significance. Nature 2019;567:305–307.)| false
• Search Google Scholar
• Export Citation

30. Grimes DA, Schulz KF. Uses and abuses of screening tests. Lancet 2002;359:881–884.

30. Grimes DASchulz KF. Uses and abuses of screening tests. Lancet 2002;359:881–884.)| false
• Search Google Scholar
• Export Citation

31. White BJ, Larson RL, Theurer ME. Interpreting statistics from published research to answer clinical and management questions. J Anim Sci 2016;94:4959–4971.

31. White BJLarson RLTheurer ME. Interpreting statistics from published research to answer clinical and management questions. J Anim Sci 2016;94:4959–4971.)| false
• Search Google Scholar
• Export Citation

32. Browner WS, Newman TB. Are all significant P values created equal? The analogy between diagnostic tests and clinical research. JAMA 1987;257:2459–2463.

32. Browner WSNewman TB. Are all significant P values created equal? The analogy between diagnostic tests and clinical research. JAMA 1987;257:2459–2463.)| false
• Search Google Scholar
• Export Citation

33. Greenland S. Bayesian perspectives for epidemiological research: I. Foundations and basic methods. Int J Epidemiol 2006;35:765–775.

33. Greenland S. Bayesian perspectives for epidemiological research: I. Foundations and basic methods. Int J Epidemiol 2006;35:765–775.)| false
• Search Google Scholar
• Export Citation

34. Lash TL. The harm done to reproducibility by the culture of null hypothesis significance testing. Am J Epidemiol 2017; 186:627–635.

34. Lash TL. The harm done to reproducibility by the culture of null hypothesis significance testing. Am J Epidemiol 2017; 186:627–635.)| false
• Search Google Scholar
• Export Citation

35. Wacholder S, Chanock S, Garcia-Closas M, et al. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst 2004;96:434–442.

35. Wacholder SChanock SGarcia-Closas M, et al. Assessing the probability that a positive report is false: an approach for molecular epidemiology studies. J Natl Cancer Inst 2004;96:434–442.)| false
• Search Google Scholar
• Export Citation

36. Held L. Reverse-Bayes analysis of two common misinterpretations of significance tests. Clin Trials 2013;10:236–242.

36. Held L. Reverse-Bayes analysis of two common misinterpretations of significance tests. Clin Trials 2013;10:236–242.)| false
• Search Google Scholar
• Export Citation

37. Goodman SN. P values, hypothesis tests, and likelihood: implications for epidemiology of a neglected historical debate. Am J Epidemiol 1993;137:485–496, discussion 497–501.

37. Goodman SN. P values, hypothesis tests, and likelihood: implications for epidemiology of a neglected historical debate. Am J Epidemiol 1993;137:485–496, discussion 497–501.)| false
• Search Google Scholar
• Export Citation

38. Gliner JA, Leech NL, Morgan GA. Problems with null hypothesis significance testing (NHST): what do the textbooks say? J Exp Educ 2002;71:83–92.

38. Gliner JALeech NLMorgan GA. Problems with null hypothesis significance testing (NHST): what do the textbooks say? J Exp Educ 2002;71:83–92.)| false
• Search Google Scholar
• Export Citation

39. Greenland S, Senn SJ, Rothman KJ, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol 2016;31:337–350.

39. Greenland SSenn SJRothman KJ, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol 2016;31:337–350.)| false
• Search Google Scholar
• Export Citation

40. Goodman S. A dirty dozen: twelve p-value misconceptions. Semin Hematol 2008;45:135–140.

40. Goodman S. A dirty dozen: twelve p-value misconceptions. Semin Hematol 2008;45:135–140.)| false
• Search Google Scholar
• Export Citation

41. Wasserstein RL, Lazar NA. The ASA's statement on p-values: context, process, and purpose. Am Stat 2016;70:129–133.

41. Wasserstein RLLazar NA. The ASA's statement on p-values: context, process, and purpose. Am Stat 2016;70:129–133.)| false
• Search Google Scholar
• Export Citation

42. Wagenmakers EJ. A practical solution to the pervasive problems of P values. Psychon Bull Rev 2007;14:779–804.

42. Wagenmakers EJ. A practical solution to the pervasive problems of P values. Psychon Bull Rev 2007;14:779–804.)| false
• Search Google Scholar
• Export Citation

43. Benjamin DJ, Berger JO, Johannesson M, et al. Redefine statistical significance. Nat Hum Behav 2018;2:6–10.

43. Benjamin DJBerger JOJohannesson M, et al. Redefine statistical significance. Nat Hum Behav 2018;2:6–10.)| false
• Search Google Scholar
• Export Citation

44. Trafimow D, Amrhein V, Areshenkoff CN, et al. Manipulating the alpha level cannot cure significance testing. Front Psychol 2018;9:699.

44. Trafimow DAmrhein VAreshenkoff CN, et al. Manipulating the alpha level cannot cure significance testing. Front Psychol 2018;9:699.)| false
• Search Google Scholar
• Export Citation

45. Altman DG, Bland JM. Diagnostic tests 2: predictive values. BMJ 1994;309:102.

45. Altman DGBland JM. Diagnostic tests 2: predictive values. BMJ 1994;309:102.)| false
• Search Google Scholar
• Export Citation

46. Matthews RAJ. Why should clinicians care about Bayesian methods? J Stat Plan Inference 2001;94:43–58.

46. Matthews RAJ. Why should clinicians care about Bayesian methods? J Stat Plan Inference 2001;94:43–58.)| false
• Search Google Scholar
• Export Citation

47. Colquhoun D. The reproducibility of research and the misinterpretation of p-values (Erratum published in R Soc Open Sci 2018;5:180100). R Soc Open Sci 2017;4:171085.

47. Colquhoun D. The reproducibility of research and the misinterpretation of p-values (Erratum published in R Soc Open Sci 2018;5:180100). R Soc Open Sci 2017;4:171085.)| false
• Search Google Scholar
• Export Citation

48. Ten Hagen KG. Novel or reproducible: that is the question. Glycobiology 2016;26:429.

48. Ten Hagen KG. Novel or reproducible: that is the question. Glycobiology 2016;26:429.)| false
• Search Google Scholar
• Export Citation

49. Mogil JS, Macleod MR. No publication without confirmation. Nature 2017;542:409–411.

49. Mogil JSMacleod MR. No publication without confirmation. Nature 2017;542:409–411.)| false
• Search Google Scholar
• Export Citation