I've seen the following equivalent statements about p-values:

  • "It is the probability of wrongly rejecting the null hypothesis if it is in fact true." [1]

  • "The P value or calculated probability is the estimated probability of rejecting the null hypothesis (H0) of a study question when that hypothesis is true." [2]

Some people I know insist these are wrong, and say these are equivalent to type I error. But the type I error is in fact determined by the significance level. Are these right or wrong?

[1] http://www.stats.gla.ac.uk/steps/glossary/hypothesis_testing.html#pvalue

[2] http://www.statsdirect.com/help/default.htm#basics/p_values.htm

  • $\begingroup$ This previous discussion might help. $\endgroup$ Mar 6, 2014 at 20:42

1 Answer 1


There are two p-values of interest. The critical p-value, also known as the $\alpha$-level or significance level, is decided and fixed before the study / analysis is performed. This critical p-value is in fact the probability of Type I error. Your examples are talking about the critical p-value, and I agree with those who are saying they are incorrect or at least misleading, because in common usage "the p-value" refers to the next type of p-value.

The observed p-value, or calculated p-value, is calculated as part of a statistical test. The observed p-value is the probability under the null hypothesis of observing data that leads to a test statistic as or more extreme than the test statistic actually observed in the experiment. The definition of "extreme" varies depending on your test, e.g. one-sided versus two-sided. The common usage of the phrase "the p-value" usually refers to the observed p-value.

We reject the null hypothesis if the observed p-value is less than or equal to the critical p-value. This is because, under the null hypothesis, the observed p-value is uniformly distributed on $[0,1]$. Thus the probability that we reject the null when in fact the null is true is equal to the critical p-value.

Edit: The full section in [1] is talking about the observed p-values, but the one line you quoted only makes sense when talking about the critical p-value. If you were to force it to be about observed p-values and be correct, it would be something like "It is the probability of a new, identical study wrongly rejecting the null hypothesis if you set your alpha-level equal to it." Strictly speaking, once you've done your analysis, the probability of wrongly rejecting the null hypothesis is either 0 or 1, depending on whether or not the null hypothesis is true and whether or not you rejected it!

  • $\begingroup$ Thanks. But 1 seems to make the statement I quoted about (observed) p-values, not significance levels. Hence the confusion. $\endgroup$
    – John Tyson
    Mar 6, 2014 at 20:51
  • $\begingroup$ I've updated my answer to address this. $\endgroup$
    – P Schnell
    Mar 6, 2014 at 21:06

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