P-value

Not to be confused with the P-factor.

In null-hypothesis significance testing, the p-value[note 1] is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct.[2][3] A very small p-value means that such an extreme observed outcome would be very unlikely under the null hypothesis. Even though reporting p-values of statistical tests is common practice in academic publications of many quantitative fields, misinterpretation and misuse of p-values is widespread and has been a major topic in mathematics and metascience.[4][5]

In 2016, the American Statistical Association (ASA) made a formal statement that "p-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone" and that "a p-value, or statistical significance, does not measure the size of an effect or the importance of a result" or "evidence regarding a model or hypothesis".[6] That said, a 2019 task force by ASA has issued a statement on statistical significance and replicability, concluding with: "p-values and significance tests, when properly applied and interpreted, increase the rigor of the conclusions drawn from data".[7]

  1. ^ "ASA House Style" (PDF). Amstat News. American Statistical Association.
  2. ^ Aschwanden C (2015-11-24). "Not Even Scientists Can Easily Explain P-values". FiveThirtyEight. Archived from the original on 25 September 2019. Retrieved 11 October 2019.
  3. ^ Wasserstein RL, Lazar NA (7 March 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.
  4. ^ Hubbard R, Lindsay RM (2008). "Why P Values Are Not a Useful Measure of Evidence in Statistical Significance Testing". Theory & Psychology. 18 (1): 69–88. doi:10.1177/0959354307086923. S2CID 143487211.
  5. ^ Munafò MR, Nosek BA, Bishop DV, Button KS, Chambers CD, du Sert NP, et al. (January 2017). "A manifesto for reproducible science". Nature Human Behaviour. 1 (1): 0021. doi:10.1038/s41562-016-0021. PMC 7610724. PMID 33954258. S2CID 6326747.
  6. ^ Wasserstein, Ronald L.; Lazar, Nicole A. (2016-04-02). "The ASA Statement on p -Values: Context, Process, and Purpose". The American Statistician. 70 (2): 129–133. doi:10.1080/00031305.2016.1154108. ISSN 0003-1305. S2CID 124084622.
  7. ^ Benjamini, Yoav; De Veaux, Richard D.; Efron, Bradley; Evans, Scott; Glickman, Mark; Graubard, Barry I.; He, Xuming; Meng, Xiao-Li; Reid, Nancy M.; Stigler, Stephen M.; Vardeman, Stephen B.; Wikle, Christopher K.; Wright, Tommy; Young, Linda J.; Kafadar, Karen (2021-10-02). "ASA President's Task Force Statement on Statistical Significance and Replicability". Chance. 34 (4). Informa UK Limited: 10–11. doi:10.1080/09332480.2021.2003631. ISSN 0933-2480.


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