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Do Baseline p values follow a uniform distribution in Randomised trials?
The notion that P-values for comparisons of groups using baseline data in randomised clinical trials should follow a Uniform distribution if the randomisation is valid has been found to be true only in the context of independent variables which follow a Normal distribution, not for Lognormal data, correlated variables.
Which distribution should be used to calculate the p-value?
sampling distribution
The p-value is calculated using the sampling distribution of the test statistic under the null hypothesis, the sample data, and the type of test being done (lower-tailed test, upper-tailed test, or two-sided test).
How do we calculate the p-value?
If your test statistic is positive, first find the probability that Z is greater than your test statistic (look up your test statistic on the Z-table, find its corresponding probability, and subtract it from one). Then double this result to get the p-value.
Is the distribution of p-values uniformly distributed?
The upper-left plot corresponds the distribution of p-values for a continuous . Note that it is distributed uniformly. Now you should note that the upper-right and lower-left plots are far from being uniformly distributed.
Why do we use a uniform p-value?
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.
Why does a null hypothesis have a uniform distribution?
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 α for any conditions where the null is true. Under the null hypothesis, your test statistic T has the distribution F ( t) (e.g., standard normal).
When does the uniform distribution become the limiting distribution?
The uniform becomes the limiting distribution as μ 1 gets closer to μ 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).