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tnich

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If you wanted to generate random numbers from whatever the null distribution is, you would first generate a uniform random number and then apply the inverse cdf of the null distribution to get a random value. That value would be a (one-sided) p-value.

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Stephen Tashi

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Help. I need an intuitive, non-mathematical explanation of why p-values from hypothesis testing are uniformly distributed.

That won't be true if the the test statistic has a discrete distribution.

If you wanted to generate random numbers from whatever the null distribution is, you would first generate a uniform random number and then apply the inverse cdf of the null distribution to get a random value. That value would be a (one-sided) p-value.

That value would be a value ##t_0## of the test statistic. The p-value corresponding to ##t_0## would be (for a left tail test) the cdf evaluated at ##t_0## so you get back the original random number that you chose from a uniform distribution.

It looks like we're ok for a left tail test from a continuous distribution. Are things really going to work out for other types of acceptance/rejection regions?

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