I understand that hypothesis testing is essentially a statistical form of proof by contradiction. In proof by contradiction, you assume P, then show that it leads to a result Q, which you know to be false. Since we assume logical consistency, Q and ~Q cannot both be true, therefore ~P.

In hypothesis testing, you start with a hypothesis H and you make an observation, O. If you find that P(O | H) << 1, then we can say that H is unlikely, in a similar way to proof by contradiction, but this reasoning is a heuristic one. What is the formal mechanism behind this result?

How do you get from P(O | H) << 1 to P(H | O) << 1?

  • $\begingroup$ One way is if $P(O) \approx P(H)$. 😀 $\endgroup$ Dec 14, 2017 at 18:43
  • $\begingroup$ Look into the Neyman-Pearson framework for hypothesis testing. They are largely responsible for planting NHST on more rigorous mathematical grounds than had previously existed (and feuded with RA Fisher because he disagreed with their approach). As usual, Casella and Berger do a good job here if you have a copy on hand. $\endgroup$
    – klumbard
    Dec 14, 2017 at 20:12
  • $\begingroup$ Wow. How'd I manage that one? Thanks, @Semiclassical. $\endgroup$ Dec 14, 2017 at 20:15
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    $\begingroup$ It is misleading to suggest that a hypothesis test is ever a proof of anything, let alone analogous to proof by contradiction. A hypothesis test is never proof, but merely provides evidence for or against a hypothesis. It is also misleading to suggest that a hypothesis is rejected because $p(O|H)<<1$ - that isn't how hypothesis tests are conducted. $\endgroup$ Dec 14, 2017 at 21:33
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    $\begingroup$ This is a serious, controversial and complex question in statistics. I think you're unlikely to get a good answer from a few mere mathematicians. You could ask at statistics.stackexhange.com. I also strongly recommend Andrew Gelman's blog. andrewgelman.com/?s=nhst (searched for nhst). $\endgroup$ Dec 14, 2017 at 22:24

4 Answers 4


From someone who was long time ago trained as a frequentist...

For a frequentist, $P(H)$ (and the other terms such as $P(O\mid H)$ and $P(H\mid O)$) don't even make sense. Either $H$ is true or $H$ is false. You can only talk about $P(O)$ assuming $H$, or possibly $P(O)$ assuming another hypothesis $H_1$ (the latter being about a different probability space, a different random variable etc.).

If (assuming $H$) $P(O)$ turns out small (e.g. $10^{-6}$), you "reject" $H$. However, the meaning of this rejection is:

  • Not that you "know" $H$ is false (you don't),
  • Not that $H$ is "probably" false with "probability" $1-10^{-6}$ (that does not make sense to a frequentist).

The meaning is only that you can now claim that: either $H$ is false or $H$ is true and you were extremely lucky with your observation - it was one in a million.

  • $\begingroup$ I've been looking at your answer and thinking about the problem a lot. This answer is what I came up with. Thoughts? math.stackexchange.com/a/2570395/186319 $\endgroup$ Dec 17, 2017 at 12:58
  • $\begingroup$ Do frequentists believe in the concept of "confidence" (as found in "confidence intervals")? If so, then can we substitute $Confidence(H)$ for $P(H)$, etc., and get the same sort of math that Bayes' Law would give us? $\endgroup$
    – ruakh
    Dec 22, 2017 at 5:51

This is a good question!

My immediate reaction: Can't you just use Bayes' Law here?

That is, we have:

$$P(H|O) = \frac{P(O|H) \cdot P(H)}{P(O)}$$

So, while this shows that $P(H)$ and $P(O)$ are also involved, at the very least this shows that $P(O|H)$ and $P(H|O)$ are proportionally related: the lower $P(O|H)$, the lower $P(H|O)$.

Indeed, if we find $P(O|H)<<1$, then assuming 'comparable' values of $P(H)$ and $P(O)$, it would make sense that $P(H|O)<<1$

OK, but are $P(O)$ and $P(H)$ indeed 'comparable'?

Well, it can be pointed out that in practice we typically try to make $P(O)$ to be fairly low. That is, you don't want to run an experiment where a large range of hypotheses would all predict $O$, i.e. you want to make a strong prediction. For example, if we are coming up with some hypotheses as to how strong of a person I am, we are not going to run an experiment that tests whether or not I can lift a pencil (which, even if I am able to, tells us very little as to my strength) but rather one that tests whether I can lift a refridgerator (which, if true, would say a lot more)

Likewise the most useful theories are strong theories, i.e. ones for which $P(H)$ is low. A theory that says that I can lift objects up to $1$ pound is not a very useful theory.

And finally, the best experiments are crucial experiments, where each unique hypotheses is associated with its very own unique observation, making it plausible that $P(O)$ and $P(H)$ are indeed 'comparable'.

One more thought. How would it be possible that $P(O|H)<<1$ and yet we don't have that $P(H|O)<<1$? By Bayes' Law this would be when $P(H)$ is high and $P(O)$ is low (or at least $P(H)$ is comparatively much higher than $P(O)$). What would that be like? It would be some kind of context where we run an experiment that makes a prediction that is far more specific than the hypothesis ... And how could that be? Well, maybe the prediction is relying on lots of factors that have nothing to do with the hypothesis. For example, it could be that the prediction can only be made when relying on all kinds of auxiliary hypotheses that are in fact unlikely to be true (or at least: significantly decrease the probability of the prediction to be true). Or: the prediction relies on one's ability to run a super-controlled experiment ... which may be unlikely to be the case. And yes, in those kinds of cases, it does make sense to say that just because some prediction didn't come out true (i.e. we didn't observe $O$), doesn't mean that the hypothesis is unlikely, as the problem may well lie elsewhere (and here is a link with the proof by contradiction as well: if $H$ (hypothesis true) and $A$ (auxilliary hypothesis true) and $E$ (experiment perfectly executed) lead to a contradiction, we can reject $H$ .. but we could also reject $A$, or $E$. So, that is maybe the lesson here ... and why I believe your question and observation that $P(H|O)$ and $P(O|H)$ are not the same thing is an important distinction for practicing scientists to keep in mind!

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    $\begingroup$ Except that as far as I can see, hypothesis testing does not make any assumptions about P(O) or P(H). Does it? $\endgroup$ Dec 14, 2017 at 18:45
  • $\begingroup$ @DanielGoldman Well, one thing you typically do is to make sure that $P(O)$ is fairly low. That is, you don't want to run an experiment where a large range of hypotheses would all predict $O$, i.e. you want to make a strong prediction. And same for hypotheses, really: the most useful theories are strong theories, i.e. ones for which $P(H)$ is low. And the best experiments are crucial experiments, where each unique hypotheses is associated with its very own unique observation, making it plausible that $P(O)$ and $P(H)$ are indeed 'comparable'. $\endgroup$
    – Bram28
    Dec 14, 2017 at 18:47
  • $\begingroup$ I'm not sure if I've ever seen a hypothesis test where those other factors are really taken into account. $\endgroup$ Dec 14, 2017 at 19:02
  • $\begingroup$ @DanielGoldman True ... and for that reason I believe your question (and your observation that $P(H|O)$ and $P(O|H)$ are different things) is an important one that practicing scientists should pay attention to! $\endgroup$
    – Bram28
    Dec 14, 2017 at 19:07
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    $\begingroup$ @DanielGoldman I agree ... you asked a really interesting question I had never really thought about ... so what I wrote our really just thoughts as they came to mind. Made me think though, so thanks for your question!! $\endgroup$
    – Bram28
    Dec 14, 2017 at 19:37

It depends whether you ask a Bayesian or a frequentist. The Bayesian would essentially give the answer that @Bram28 gave, namely that P(O|H) and P(H|O) are related by Bayes' theorem.

Let me give the frequentist view instead. A frequentist would argue that P(H|O) does not make sense, because whether a hypothesis holds or not is not random. But what makes sense is P(O|H) and P(O|K) (when K denotes the alternative hypothesis). Loosely speaking, if one (say, the latter) of the two conditional probabilities is much larger than the other, one decides for K. Of course, the formal mechanism (involving a level, p-values, etc.) is more involved, but the core idea remains the same:

  • $\begingroup$ I feel like we should be able to argue from axioms of probability theory + logical consistency, that hypothesis testing is valid. Hmm. $\endgroup$ Dec 14, 2017 at 19:43
  • $\begingroup$ @DanielGoldman If you want an answer backed up by logical consistency, you get the bayesian framework, see e.g. Cox's theorem. If you want to go into more detail read 'Probability Theory: The Logic of Science' by E.T. Jaynes. $\endgroup$ Dec 15, 2017 at 2:18

I want to take a crack at this myself, and see if anyone thinks the answer is reasonable. user8734617's answer, I think, is essentially there, but it does not include the "so what?"

Since I assumed that hypothesis testing is a statistical version of proof by contradiction, maybe proof by contradiction can be of use.

Whenever we conduct a hypothesis test, we select a significance level, $\alpha.$ Then we assume two things. First we assume that our hypothesis H is true (frequentist approach). But H is not just a value, it is an entire distribution for that value. Then we assume that what we observe is not that unusual: the probability of whatever we observe is greater than $\alpha.$

Since we assume H is true, we can calculate the probability of our observations, based on H and compare it with $\alpha.$ If the probability is less than $\alpha$, we have a contradiction and we can say, based on direct logical consequence, that either H is false or our observation was more unusual than we were willing to tolerate.

It is the selection of the $\alpha$ which allows us to actually draw a full conclusion. Without that value, we would not be able to say much. Unfortunately this method still does not allow us to go backwards and fully say what the probability of H is, given an observation, and it would be nice to be able to connect the frequentist approach to the Bayesian approach in a robust way.


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