Thanks to Mark Andrews for correcting some crucial typos (I hope I got it right this time!).

Thanks also to Andrew Gelman for pointing out that the proof below holds only when the null hypothesis is a point null $H_0: \mu = 0$, and the dependent measure is continuous, such as reading time in milliseconds, or EEG responses.

Someone asked this question in my linear modeling class: why is it that the p-value has a uniform distribution when the null hypothesis is true? The proof is remarkably simple (and is called the probability integral transform).

First, notice that when a random variable Z comes from a $Uniform(0,1)$ distribution, then the probability that $Z$ is less than (or equal to) some value $z$ is exactly $z$: $P(Z\leq z)=z$.

Next, we prove the following proposition:

**Proposition**:

If a random variable $Z=F(T)$, then $Z \sim Uniform(0,1)$.

Note here that the p-value is a random variable, call it $Z$. The p-value is computed by calculating the probability of seeing a t-statistic or something more extreme under the null hypothesis. The t-statistic comes from a random variable $T$ that is a transformation of the random variable $\bar{X}$: $T=(\bar{X}-\mu)/(\sigma/\sqrt{n})$. This random variable T has a CDF $F$.

So, if we can prove the above proposition, we have shown that the p-value's distribution under the null hypothesis is $Uniform(0,1)$.

**Proof**:

Let $Z=F(T)$.

$P(Z\leq z) = P(F(T)\leq z) = P(F^{-1} F(T) \leq F^{-1}(z) )

= P(T \leq F^{-1} (z) )

= F(F^{-1}(z))= z$.

Since $P(Z\leq z)=z$, Z is uniformly distributed, that is, Uniform(0,1).

A screengrab in case the above doesn't render:

## 2 comments:

Am I missing a trick or is there a typo in the third term in the equation?

I.e. should P(Z <= F^-1(z)) be P(T <= F^-1(z))?

Here's how I reason out the proof (sorry about ascii math):

Let Z = F(T)

Pr { Z <= z }

= Pr {F(T) <= z } # By definition of Z = F(T).

= Pr {F^-1(F(T)) <= F^-1(z)} # Apply inverse of F to both sides.

= Pr {T <= F^-1(z)} # F and F^-1 cancel on lhs.

= F(F^-1(z)) # Because previous step defines cumulative dist function.

= z # And F and F^-1 cancel again.

One should show this to people who say/think that P( statistic | Null True ) $\approx$ P( Null True ).

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