Consider the following syllogism:

- If a person is an American, he is
**probably** not a member of Congress.
- This person is a member of Congress.
- Therefore he is
**probably** not American.

As John D. Cook writes:

We can’t reject a null hypothesis just because we’ve seen data that are rare under this hypothesis. Maybe our data are even more rare under the alternative. It is rare for an American to be in Congress, but it is even more rare for someone who is not American to be in the US Congress!

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April 28, 2011 at 8:54 am

SimonI dont see the relevance of this to hypothesis testing. If you let A be the event a person is American, B be the event a person is a member of congress, then we are given that P(B | A) is low. From this you conclude that P( A | B ) is low? This does not follow logically.

April 28, 2011 at 9:20 am

jeffI think the point is this. If I write down a statistical model with a null hypothesis and I know the distribution of data generated by the null, then I will reject the null if according to that distribution the sample I observed has low probability. That is problematic since as you point out it does not follow from the laws of conditional probability that the null hypothesis is (likely to be) invalid. It could be that the sample that has even lower probability under the alternative hypothesis.

Obviously the syllogism is logically flawed, but it represents an example of a deduction that hypothesis testing would lead you to.

April 28, 2011 at 11:38 am

AndyPower calculations would quickly uncover the problem.

April 28, 2011 at 6:14 pm

JeremyJeff is entirely right that it depends on the alternative hypothesis. As Simon writes, let A denote person is American and let B denote person is member of congress.

Pr(A|B) = PR(B|A)Pr(A) / [ Pr(B|A)Pr(A) + Pr(B|notA)Pr(notA)]

The alternative hypothesis that Jeff mentions is Pr(B|notA).

We’re told from the setup of the problem that Pr(B|A) is low. If it turns out that all 435 members of congress are American and no member of congress is not an American, then Pr(B|notA)=0 and so Pr(A|B)=1.0.

But suppose that some members of congress aren’t Americans. In the abstract, suppose that no member of congress is American (some people argue none of them really are). Then surely Pr(B|A)=0 and Pr(B|notA)=1, and so Pr(A|B)=0. Of course, just because no member of congress is American doesn’t mean one couldn’t be. But that aside, the interesting result happens here that Pr(A|B) just ends up being the share of congress members that are American. That’s entirely intuitive, explaining the kneejerk reaction to the syllogism.

On a more esoteric note, responding to Simon, the syllogism is relevant to hypothesis testing. Of course the syllogism holds under standard Classical (with a big C) hypothesis testing, which ignores the likelihood of the data under alternative hypotheses. This is why some folks advocate for Bayesian hypothesis testing instead, which gets you to Pr(A|B), just as Simon points out.