# Why are p-values uniformly distributed under the null hypothesis?

Recently, I have found in a paper by Klammer, et al. a statement that p-values should be uniformly distributed. I believe the authors, but cannot understand why it is so.

Klammer, A. A., Park, C. Y., and Stafford Noble, W. (2009) Statistical Calibration of the SEQUEST XCorr Function. Journal of Proteome Research. 8(4): 2106–2113.

• This is immediate from the definition of the p-value as the probability integral transform of the test statistic using the distribution under the null hypothesis. The conclusion requires that the distribution be continuous. When the distribution is discrete (or has atoms), the distribution of p-values is discrete, too, and therefore can only approximately be uniform. – whuber May 10 '11 at 18:46
• @whuber gave the answer which was something I suspected. I asked the original reference just to be sure that something was not lost in translation. Usually it does not matter whether the article is specific or not, statistical content always shows through :) – mpiktas May 10 '11 at 18:56
• Only when $H_0$ is true! ... and more strictly, only when continuous (though something like it is true in the non-continuous case; I don't know the right word for the most general case; it's not uniformity). Then it follows from the definition of p-value. – Glen_b Jun 7 '13 at 1:35
• This could be seen as a variant of the fundamental statistical mechanics principle (that students often have similar difficulty accepting) that all micro-states of a physical system have equal probability. – DWin Jul 21 '13 at 19:43
• How about the claim in this article: plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0076010 ? – user54876 Aug 28 '14 at 18:26

To clarify a bit. The p-value is uniformly distributed when the null hypothesis is true and all other assumptions are met. The reason for this is really the definition of alpha as the probability of a type I error. We want the probability of rejecting a true null hypothesis to be alpha, we reject when the observed $\text{p-value} < \alpha$, the only way this happens for any value of alpha is when the p-value comes from a uniform distribution. The whole point of using the correct distribution (normal, t, f, chisq, etc.) is to transform from the test statistic to a uniform p-value. If the null hypothesis is false then the distribution of the p-value will (hopefully) be more weighted towards 0.

The Pvalue.norm.sim and Pvalue.binom.sim functions in the TeachingDemos package for R will simulate several data sets, compute the p-values and plot them to demonstrate this idea.

Also see:

Murdoch, D, Tsai, Y, and Adcock, J (2008). P-Values are Random Variables. The American Statistician, 62, 242-245.

for some more details.

# Edit:

Since people are still reading this answer and commenting, I thought that I would address @whuber's comment.

It is true that when using a composite null hypothesis like $\mu_1 \leq \mu_2$ that the p-values will only be uniformly distributed when the 2 means are exactly equal and will not be a uniform if $\mu_1$ is any value that is less than $\mu_2$. This can easily be seen using the Pvalue.norm.sim function and setting it to do a one sided test and simulating with the simulation and hypothesized means different (but in the direction to make the null true).

As far as statistical theory goes, this does not matter. Consider if I claimed that I am taller than every member of your family, one way to test this claim would be to compare my height to the height of each member of your family one at a time. Another option would be to find the member of your family that is the tallest and compare their height with mine. If I am taller than that one person then I am taller than the rest as well and my claim is true, if I am not taller than that one person then my claim is false. Testing a composite null can be seen as a similar process, rather than testing all the possible combinations where $\mu_1 \leq \mu_2$ we can test just the equality part because if we can reject that $\mu_1 = \mu_2$ in favour of $\mu_1 > \mu_2$ then we know that we can also reject all the possibilities of $\mu_1 < \mu_2$. If we look at the distribution of p-values for cases where $\mu_1 < \mu_2$ then the distribution will not be perfectly uniform but will have more values closer to 1 than to 0 meaning that the probability of a type I error will be less than the selected $\alpha$ value making it a conservative test. The uniform becomes the limiting distribution as $\mu_1$ gets closer to $\mu_2$ (the people who are more current on the stat-theory terms could probably state this better in terms of distributional supremum or something like that). So by constructing our test assuming the equal part of the null even when the null is composite, then we are designing our test to have a probability of a type I error that is at most $\alpha$ for any conditions where the null is true.

• Sorry for the typo I introduced (should read \leq in TeX)! – chl May 11 '11 at 7:25
• The article "P-Values are Random Variables" is really interesting, is there any introductory book that adheres to the principles stated in the article? – Alessandro Jacopson Jun 30 '11 at 13:09
• Despite the comment I posted to the question, I have since realized that the conclusion is not true except in special cases. The problem occurs with composite hypotheses, such as $\mu_1 \le \mu_2$. "The null hypothesis is true" now covers many possibilities, such as the case $\mu_1 = \mu_2 - 10^6$. In such a case, the p-values will not be uniformly distributed. I suspect one could manufacture (somewhat artificial) situations in which, no matter what element of the null hypothesis holds, the distribution of p-values would never be anywhere near uniform. – whuber Jul 20 '12 at 14:50
• @Greg Snow: I think that the distribution of the p-values is not always uniform, it is uniform when they are computed from a continuous distribution, but not when they are computed from a discrete distribution – user83346 Aug 16 '15 at 16:58
• I have expanded the answer above to address the comment by @whuber. – Greg Snow Aug 17 '15 at 15:42

Under the null hypothesis, your test statistic $T$ has the distribution $F(t)$ (e.g., standard normal). We show that the p-value $P=F(T)$ has a probability distribution $$\begin{equation*} \Pr(P < p) = \Pr(F^{-1}(P) < F^{-1}(p)) = \Pr(T < t) \equiv p; \end{equation*}$$ in other words, $P$ is distributed uniformly. This holds so long as $F(\cdot)$ is invertible, a necessary condition of which is that $T$ is not a discrete random variable.

This result is general: the distribution of an invertible CDF of a random variable is uniform on $[0,1]$.

• you might want to rephrase your last comment, which is a little confusing. Continuous CDFs do not necessarily have a (proper) inverse. (Can you think of a counterexample?) So your proof requires additional conditions to hold. The standard way to get around this is to define the pseudoinverse $F^{\,\leftarrow}(y) = \inf\{x: F(x) \geq y\}$. The argument becomes more subtle, too. – cardinal May 26 '11 at 23:36
• Concerning working with generalized inverses, see link.springer.com/article/10.1007%2Fs00186-013-0436-7 (in particular, F(T) is only uniform if F is continuous -- doesn't matter whether F is invertible or not). Concerning your definition of a p-value: I don't think it's always 'F(T)'. It's the probability (under the null) of taking on a value more extreme than the observed one, so it could also be the survival function (just to be precise here). – Marius Hofert Mar 5 '16 at 9:03
• Isn't $F(t)$ the CDF? – zyxue May 2 '18 at 21:58
• @zyxue Yes, the cdf is sometimes referred to as the "distribution". – mikario Sep 22 '18 at 2:50

Simple simulation of distribution of p-values in case of linear regression between two independent variables :

# estimated model is: y = a0 + a1*x + e

obs<-100                # obs in each single regression
Nloops<-1000            # number of experiments
output<-numeric(Nloops) # vector holding p-values of estimated a1 parameter from Nloops experiments

for(i in seq_along(output)){

x<-rnorm(obs)
y<-rnorm(obs)

# x and y are independent, so null hypothesis is true
output[i] <-(summary(lm(y~x)) $coefficients)[2,4] # we grab p-value of a1 if(i%%100==0){cat(i,"from",Nloops,date(),"\n")} # after each 100 iteration info is printed } plot(hist(output), main="Histogram of a1 p-values") ks.test(output,"punif") # Null hypothesis is that output distr. is uniform  • Could you elaborate on how this answers the question? Although its output illustrates a special case of the assertion, no amount of code would be capable of addressing the question of why? That requires additional explanation. – whuber Jun 2 '15 at 14:11 Let$T$denote the random variable with cumulative distribution function$F(t) \equiv \Pr(T<t)$for all$t$. Assuming that$F$is invertible we can derive distribution of the random p-value$P = F(T)$as follows: $$\Pr(P<p) = \Pr(F(T) < p) = \Pr(T < F^{-1}(p)) = F(F^{-1}(p)) = p,$$ from which we can conclude that the distribution of$P$is uniform on$[0,1]$. This answer is similar to Charlie's, but avoids having to define$t = F^{-1}(p)\$.

If p values are uniformly distributed under the H0 that means that it is as likely to see a p-value of .05 as a p-value of .80, but this is not true, as it is less likely to observe a p-value of .05 than a p-value of .80, because that is precisely the definition of the normal distribution from which the p-value are taken. There will be more samples fallling within the range of normality than outside it, by definition. Therefore, more likely to find larger p-values than smaller ones.

• -1. This is completely wrong. I wonder who upvoted this. P-values under point H0 are uniformly distributed. – amoeba Nov 19 '17 at 9:52
• -1. This doesn't even make enough sense to be called wrong: "range of normality" is meaningless and p-values inherently have nothing to do with normal distributions in the first place. – whuber Nov 19 '17 at 16:45

## protected by kjetil b halvorsenMay 11 '18 at 22:16

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