Editing P-value (section)

=== Distribution ===

The ''p''-value is a function of the chosen test statistic <math>T</math> and is therefore a [[random variable]]. If the null hypothesis fixes the probability distribution of <math>T</math> precisely (e.g. <math>H_0: \theta = \theta_0,</math> where <math>\theta</math> is the only parameter), and if that distribution is continuous, then when the null-hypothesis is true, the ''p''-value is [[Uniform distribution (continuous)|uniformly distributed]] between 0 and 1. Regardless of the truth of the <math>H_0</math>, the ''p''-value is not fixed; if the same test is repeated independently with fresh data, one will typically obtain a different ''p''-value in each iteration.

Usually only a single ''p''-value relating to a hypothesis is observed, so the ''p''-value is interpreted by a significance test, and no effort is made to estimate the distribution it was drawn from. When a collection of ''p''-values are available (e.g. when considering a group of studies on the same subject), the distribution of ''p''-values is sometimes called a ''p''-curve.<ref name="Head2015">{{cite journal | vauthors = Head ML, Holman L, Lanfear R, Kahn AT, Jennions MD | title = The extent and consequences of p-hacking in science | journal = PLOS Biology | volume = 13 | issue = 3 | pages = e1002106 | date = March 2015 | pmid = 25768323 | pmc = 4359000 | doi = 10.1371/journal.pbio.1002106 | doi-access = free }}</ref>
A ''p''-curve can be used to assess the reliability of scientific literature, such as by detecting publication bias or [[p-hacking|''p''-hacking]].
<ref name="Head2015"/><ref name="Simonsohn2014">{{cite journal | vauthors = Simonsohn U, Nelson LD, Simmons JP | title = ''p''-Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results | journal = Perspectives on Psychological Science | volume = 9 | issue = 6 | pages = 666–681 | date = November 2014 | pmid = 26186117 | doi = 10.1177/1745691614553988 | s2cid = 39975518 }}</ref>