Misinterpretations of P-values and statistical tests persists among researchers and professionals working with statistics and epidemiology

Keywords: Statistical inference, null hypothesis significance testing, statistics, frequentist, P-value


Background: The aim was to investigate inferences of statistically significant test results among persons with more or less statistical education and research experience.

Methods: A total of 75 doctoral students and 64 statisticians/epidemiologist responded to a web questionnaire about inferences of statistically significant findings. Participants were asked about their education and research experience, and also whether a ‘statistically significant’ test result (P = 0.024, α-level 0.05) could be inferred as proof or probability statements about the truth or falsehood of the null hypothesis (H0) and the alternative hypothesis (H1).

Results: Almost all participants reported having a university degree, and among statisticians/epidemiologist, most reported having a university degree in statistics and were working professionally with statistics. Overall, 9.4% of statisticians/epidemiologist and 24.0% of doctoral students responded that the statistically significant finding proved that H0 is not true, and 73.4% of statisticians/epidemiologists and 53.3% of doctoral students responded that the statistically significant finding indicated that H0 is improbable. Corresponding numbers about inferences about the alternative hypothesis (H1) were 12.0% and 6.2% about proving H1 being true and 62.7 and 62.5% for the conclusion that H1 is probable. Correct inferences to both questions, which is that a statistically significant finding cannot be inferred as either proof or a measure of a hypothesis’ probability, were given by 10.7% of doctoral students and 12.5% of statisticians/epidemiologists.

Conclusions: Misinterpretation of P-values and statistically significant test results persists also among persons who have substantial statistical education and who work professionally with statistics.


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1. Nickerson RS. Null hypothesis significance testing: a review of an old and continuing controversy. Psychol Methods. 2000;5:241–301. doi: 10.1037/1082-989X.5.2.241

2. Greenland S, Senn SJ, Rothman KJ, Carlin JB, Poole C, Goodman SN, et al. Statistical tests, P values, confidence intervals, and power: a guide to misinterpretations. Eur J Epidemiol. 2016;31:337–50. doi: 10.1007/s10654-016-0149-3

3. Amrhein V, Trafimow D, Greenland S. Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication. The American Statistician. 2019;73(sup1):262–70. doi: 10.1080/00031305.2018.1543137

4. Rozeboom WW. The fallacy of the null-hypothesis significance test. Psychol Bull. 1960;57:416–28. doi: 10.1037/h0042040

5. Oakes M. Statistical inference: a commentary for the social and behavioral sciences. Chichester, UK: John Wiley & Sons; 1986.

6. Wasserstein RL, Schirm AL, Lazar NA. Moving to a World Beyond “p < 0.05”. The American Statistician. 2019;73(sup1):1–19. doi: 10.1080/00031305.2019.1583913

7. Bohlmeijer ET, Fledderus M, Rokx TA, Pieterse ME. Efficacy of an early intervention based on acceptance and commitment therapy for adults with depressive symptomatology: Evaluation in a randomized controlled trial. Behav Res Ther. 2011;49:62–7. doi: 10.1016/j.brat.2010.10.003

8. Goodman SN. Toward evidence-based medical statistics. 1: The P value fallacy. Ann Intern Med. 1999;130:995–1004. doi: 10.7326/0003-4819-130-12-199906150-00008

9. Gigerenzer G. Mindless statistics. The Journal of Socio-Economics. 2004;33:587–606. doi: 10.1016/j.socec.2004.09.033

10. Szucs D, Ioannidis JPA. When Null Hypothesis Significance Testing Is Unsuitable for Research: A Reassessment. Front Hum Neurosci. 2017;11:390. doi: 10.3389/fnhum.2017.00390

11. Stang A, Poole C, Kuss O. The ongoing tyranny of statistical significance testing in biomedical research. Eur J Epidemiol. 2010;25:225–30. doi: 10.1007/s10654-010-9440-x

12. Wasserstein RL, Lazar NA. The ASA’s statement on p-values: context, process, and purpose. The American Statistician. 2016;70:129–33. doi: 10.1080/00031305.2016.1154108

13. Baker M. Statisticians issue warning over misuse of P values. Nature. 2016;531:151. doi: 10.1038/nature.2016.19503

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

15. Amrhein V, Greenland S, McShane B. Scientists rise up against statistical significance. Nature. 2019;567:305–7. doi: 10.1038/d41586-019-00857-9

16. Lytsy P. P in the right place: Revisiting the evidential value of P-values. J Evid Based Med. 2018;11:288–91. doi: 10.1111/jebm.12319

17. Goodman S. A dirty dozen: twelve p-value misconceptions. Semin Hematol. 2008;45:135–40. doi: 10.1053/j.seminhematol.2008.04.003

18. Cohen J. The Earth is Round (p < .05). American Psychologist. 1994;49:997–1003. doi: 10.1037/0003-066X.49.12.997

19. Falk R, Greenbaum CW. Significance Tests Die Hard: The Amazing Persistence of a Probabilistic Misconception. Theory & Psychology. 1995;5:75–98. doi: 10.1177/0959354395051004

20. McShane BB, Gal D. Statistical Significance and the Dichotomization of Evidence. Journal of the American Statistical Association. 2017;112:885–95. doi: 10.1080/01621459.2017.1289846

21. Badenes-Ribera L, Frias-Navarro D, Monterde-i-Bort H, Pascual-Soler M. Interpretation of the p value: A national survey study in academic psychologists from Spain. Psicothema. 2015;27:290–5.

22. Eddy DM. Probabilistic reasoning in clinical medicine: Problems and opportunities. In: Kahneman D, Slovic P and Tversky A, eds. Judgment under uncertainty: Heuristics and biases. New York: Cambridge University Press; 1982, pp. 249–67.

23. Grimes DR. Proposed mechanisms for homeopathy are physically impossible. Focus on Alternative and Complementary Therapies. 2012;17:149–55. doi: 10.1111/j.2042-7166.2012.01162.x

24. Committee SaT. Fourth Report. Evidence Check 2: Homeopathy. London: House of Commons; 2010.
How to Cite
Lytsy P., Hartman M., & Pingel R. (2022). Misinterpretations of P-values and statistical tests persists among researchers and professionals working with statistics and epidemiology. Upsala Journal of Medical Sciences, 127(1). https://doi.org/10.48101/ujms.v127.8760