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CounterexampleConstruction: AI-generatedVerification: AI-generatedPipeline-generatedjudge pass (gpt-6-luna)
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A measurable [0,1]-valued statistic need not be a valid p-value

Statement refuted

Every measurable statistic p:X→[0,1] is a valid p-value for a given null hypothesis.

Facts & Assumptions

Given: A statistical experiment with a nonempty null hypothesis and a disjoint nonempty alternative, and the definition of a valid p-value.

[F1]

Statistical experiments: an experiment supplies a measurable data space and a probability law for each parameter.

[F2]

Valid p-values: a valid p-value must satisfy Pθ(p(X)≤u)≤u for every null parameter and every u∈[0,1].

[F3]

Test size and power: for the simple null Θ0={θ0} and a nonempty disjoint alternative, size is the rejection probability under Pθ0.

[F4]

A measurable function between measurable spaces: a statistic is measurable when all measurable target sets have measurable preimages.

[F5]

The expectation of an indicator is the probability of the event: the expectation of an event indicator is the event's probability.

[F6]

Every measurable rejection function defines a randomized test: every measurable function into [0,1] defines a binary randomized test.

Counterexample

1.1givenF1F4algebraconstruct

Let X={0,1} with the full sigma-algebra, Θ={θ0,θ1}, Θ0={θ0}, and Θ1={θ1}. Set Pθ0({0})=Pθ0({1})=1/2 and Pθ1({0})=0, Pθ1({1})=1. These masses define probability measures: they are nonnegative and sum to one, and on the two-point space countable additivity follows because at most two members of a disjoint family are nonempty. Define p(0)=0 and p(1)=1. It is measurable because every subset of X is measurable.

2.1F2F3F5F6step 1.1algebra∎

At u=1/4, {x:p(x)≤u}={0}, so Pθ0(p(X)≤1/4)=1/2>1/4. Thus p is not valid by [F2]. Thresholding it at 1/4 defines a test by [F6], rejects on {0}, and has size 1/2 by [F3,F5], so it is anti-conservative. The claimed implication is false.

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