P-value

From MEpedia, a crowd-sourced encyclopedia of ME and CFS science and history
The printable version is no longer supported and may have rendering errors. Please update your browser bookmarks and please use the default browser print function instead.

A p value is a value calculated from a research study, which is intended to show whether an outcome is likely to have occurred by chance, when taking into consideration a number of other factors.[1][2] P values are not actually defined as a probability.[3] P-hacking is the manipulation of research data to produce a clinically significant result for the calculated p value; meaning manipulating research results in order to achieve p < 0.05 rather than publishing a null result.[citation needed]

Theory

Evidence

Null hypothesis significance testing

Articles and blogs

Notable studies

  • 2012, A peculiar prevalence of p values just below .05[4] (Abstract)
  • 2013, The life of p: “Just significant” results are on the rise[5] (Abstract)
  • 2015, Blinded by the Light: How a Focus on Statistical “Significance” May Cause p-Value Misreporting and an Excess of p-Values Just Below .05 in Communication Science[7] (Full text)
  • 2016, Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking[8] (Full text)

See also

Learn more

References

  1. "Miller-Keane Encyclopedia and Dictionary of Medicine, Nursing, and Allied Health". TheFreeDictionary.com (7th ed.). 2003. Retrieved April 21, 2019.
  2. Merrian-Webster Dictionary. "Definition of P VALUE". Merrian-Webster Dictionary. Retrieved April 21, 2019.
  3. 3.0 3.1 Kuffner, Todd A.; Walker, Stephen G. (January 2, 2019). "Why are p-Values Controversial?". The American Statistician. 73 (1): 1–3. doi:10.1080/00031305.2016.1277161. ISSN 0003-1305.
  4. Masicampo, E.J.; Lalande, Daniel R. (November 2012). "A peculiar prevalence of p values just below .05". Quarterly Journal of Experimental Psychology. 65 (11): 2271–2279. doi:10.1080/17470218.2012.711335. ISSN 1747-0218.
  5. Leggett, Nathan C.; Thomas, Nicole A.; Loetscher, Tobias; Nicholls, Michael E.R. (December 1, 2013). "The life of p: "Just significant" results are on the rise". The Quarterly Journal of Experimental Psychology. 66 (12): 2303–2309. doi:10.1080/17470218.2013.863371. ISSN 1747-0218. PMID 24205936.
  6. Lakens, Daniël (April 1, 2015). "Comment: What p-hacking really looks like: A comment on Masicampo and LaLande (2012)". Quarterly Journal of Experimental Psychology. 68 (4): 829–832. doi:10.1080/17470218.2014.982664. ISSN 1747-0218.
  7. Vermeulen, Ivar; Beukeboom, Camiel J.; Batenburg, Anika; Avramiea, Arthur; Stoyanov, Dimo; van de Velde, Bob; Oegema, Dirk (October 2, 2015). "Blinded by the Light: How a Focus on Statistical "Significance" May Cause p-Value Misreporting and an Excess of p-Values Just Below .05 in Communication Science". Communication Methods and Measures. 9 (4): 253–279. doi:10.1080/19312458.2015.1096333. ISSN 1931-2458.
  8. van Assen, Marcel A. L.M.; van Aert, Robbie C.M.; Bakker, Marjan; Augusteijn, Hilde E.M.; Veldkamp, Coosje L.S.; Wicherts, Jelte M. (2016). "Degrees of Freedom in Planning, Running, Analyzing, and Reporting Psychological Studies: A Checklist to Avoid p-Hacking". Frontiers in Psychology. 7. doi:10.3389/fpsyg.2016.01832. ISSN 1664-1078.