# What about the "p-value" of the non-null hypothesis?

So my understanding is that the p-value is the likelihood of observing an effect at least as extreme as that shown in the sample data, if the null hypothesis is true.

But how is this a useful value? Even if the p-value is low, couldn't we have a similar, or even lower likelihood of observing an extreme effect if the null hypothesis is false?

• Consider it kind of a “What the heck??? I guess I was wrong about $H_0$!”
– Dave
Nov 26 '20 at 4:35
• Why is low p-value by itself evidence against $H_0$ though? How do I know the data isn't just rare under any hypothesis? Nov 26 '20 at 5:08
• One reason is that usually the alternative hypothesis ($H_a$) is not a fixed value and therefore, the distribution of the statistic under $H_a$ is not really known, so as to allow us to calculate the p-value. However, type 2 error can of course be calculated for some value $\mu \in H_a$. Nov 26 '20 at 5:14
• obligatory xkcd: xkcd.com/1132 Nov 26 '20 at 9:14
• Note that the alternative hypothesis to "$H_0$: parameter is $p_0$" is not even well defined, because "parameter is not $p_0$" can mean any value, even almost identical to $p_0$. If you are interested which parametr values are reasonably consistent with your data, do not use hypothesis testing, but confidence intervals. Nov 26 '20 at 9:33

## 4 Answers

Note this answer used to be much longer, but was also very verbose. So I have deleted all of it. If you like to read more then check version 2 of this post

Why do we need p-values? We need them for the cases when the experiment is subject to variability. Our measurements are not just clear examples of some observed effects. Often, measurements are noisy and random behavior is underlying observations. In such cases, we may wish to express/quantify how unlikely it would be that a certain effect is being observed if the null (no effect) hypothesis would be true. The p-value is an indication for the type I error probability.

In most settings, the estimated effect corresponds to the maximum likelihood estimate (or some method that is similar). So in this setting, there is no situation that "couldn't we have a similar or even lower likelihood..." because the effect is the effect/theory for which the likelihood is maximized.

The reason that we use the p-value is to express how precise the measurement/experiment is, by expressing how likely a measurement of this effect size could have occurred by pure chance. If the observed effect (or anything of similar size) could have likely occurred even when there is no effect present, then the experiment is not very accurate.

See for some related topics:

• Version 2 of this answer was helpful - "power" is essentially what I was concerned about. So it seems like it does depend on the test, rather than p-value being meaningful in the abstract as a standalone concept Nov 26 '20 at 19:02

You're right - the p-value is a heuristic which needs to be interpreted in context. It should be interpreted as meaning how surprising the data would be if $$H_0$$ is true. So if p is small, alternative hypotheses look more attractive.

According to Bayes' Theorem:

$$\mathbb{P}(H_0|D) = \frac{\mathbb{P}(D|H_0)\mathbb{P}(H_0)}{\mathbb{P}(D|H_0)\mathbb{P}(H_0)+\mathbb{P}(D|H_1)\mathbb{P}(H_1)}$$

So if you actually want to calculate the probability that the null hypothesis is true, your calculation has to include how likely the observed data $$D$$ is given $$H_0$$ and how likely $$D$$ is given $$H_1$$, and also a prior probability $$\mathbb{P}(H_0)$$ for how likely the null hypothesis is to be true.

If a study uses a p-value rather than Bayesian analysis to support rejection of a null hypothesis, that's probably fine so long as a) the data is sufficiently more likely under $$H_1$$ than under $$H_0$$ and b) $$H_1$$ is sufficiently plausible (so that $$\mathbb{P}(H_1)$$ isn't too small).

• Bayesian George Box wrote correctly "all models are wrong but some are useful", and de Finetti wrote that "(objective) probability does not exist" - I agree with this and think therefore that it is inappropriate to give a "probability that a model is true". It won't in reality, that probability is zero. Proper subjectivist Bayesians would not interpret a posterior probability in this way, but rather in terms of belief regarding future outcomes. Nov 26 '20 at 10:33
• I do agree that most models have exactly zero probability of being true. But I think a hypothesis like "the medicine increases the recovery rate by over 50%" can be true. Nov 26 '20 at 11:01
• Let's say this hypothesis can state something that is reasonable to believe regarding observable future outcomes. However a statistical model would involve probabilitistic assumptions such as i.i.d. and infinite replicability to even define what a 50% rate means, and such a thing cannot be literally true, at least not in any way verifiable by anything observable. Nov 26 '20 at 11:06
• Yup, it's the P(D | H_1) term I was worried about. So I guess the meaningfulness of p-value is dependent on the test (whether it satisfies condition a)) rather than being meaningful in the abstract (apart saying from how surprising the data would be) Nov 26 '20 at 18:56

First let's take an example for reference. Assume that you are testing whether mean of a population is $$0$$ and sample mean is $$\bar x$$. Further, to keep things simple assume that the true distribution of population is $$N(\mu,\sigma^2)$$, with $$\sigma$$ known.

$$H_0: \mu = 0$$ $$H_a: \mu \ne 0$$

Now, you say:

Even if the p-value is low, couldn't we have a similar, or even lower likelihood of observing an extreme effect if the null hypothesis is false?

Hypothesis testing to check how confidently you can say that $$\mu$$ is equal to a certain value. That said, it is certainly possible that we may get low p-value (lower than $$\alpha$$) for a variety of possible values of $$\mu$$. In fact the set of all such values is the complement set of the confidence interval for $$\mu$$.

In the example above, we get confidence interval (at $$5\%$$ level of significance) by following equation:

\begin{align} Pr \Big(-1.96 <\frac{\bar x - \mu}{\sigma^2}<1.96\Big) &= 0.95 \\ Pr \Big(\bar x - 1.96\sigma^2 < \mu<\bar x +1.96\sigma^2\Big) &= 0.95 \end{align}

Now let, you conduct the following test: $$H_0: \mu = \theta$$ $$H_a: \mu \ne \theta$$

where, $$\theta \in \mathbb R\backslash C$$ and $$C:= [\underline{c}, \bar c]$$; $$\underline{c}=\bar x - 1.96\sigma^2, \bar c =\bar x + 1.96\sigma^2$$

As per definition, p-value for the above test is: \begin{align} p &= Pr \bigg(Z > \bigg|\frac{\bar x - \theta}{\sigma^2}\bigg| \bigg) + Pr \bigg(Z <- \bigg|\frac{\bar x - \theta}{\sigma^2}\bigg| \bigg) \tag{where Z \sim N(0,1)}\\ &= 2\bigg(1-\Phi\bigg(\bigg|\frac{\bar x - \theta}{\sigma^2}\bigg|\bigg)\bigg) \end{align}

Given the $$\Phi(x)$$ is increasing function and that either $$\theta > \bar c$$ or $$\theta <\underline c$$; $$p<0.05$$ for $$\forall \theta \in \mathbb R\backslash C$$.

Tests are normally defined within a model that is sufficiently flexible to accommodate all possible outcomes of the test statistic or the statistic on which the test is based. For example a model may be normal distributions with unknown mean and variance, and the null hypothesis may be "mean is zero". The test to be used here is a t-test, based on the mean standardised by its estimated standard error, and whatever value you observe will be compatible with a certain distribution in the model. So you may observe mean 15, variance 1, which is incompatible with the $$H_0$$: mean equals zero (i.e., producing a small p-value, unless the data set is very very small), but of course it is compatible with a normal distribution with mean 15 - and actually whatever mean you observe will be compatible with a normal distribution with that same mean value (or a mean value close to the observed one, as you could find out from computing a confidence interval), so there is always a distribution in the alternative that fits it. Meaning that if your alternative is flexible enough, a low p-value will not only indicate evidence against the $$H_0$$, but will also mean that the data are compatible with some other distribution in the alternative.

What can happen is that the data are not compatible with any normal distribution because their distributional shape is different. This however cannot be found out by the t-test, which by definition compares one normal distribution with other normal distributions. You'd need another test to find that out, with a nonparametric alternative including all kinds of distributional shapes - and again there will be some distributions (actually more than one, even in fact infinitely many, as the set of all distributions is very, very rich) that fits the data (of course not meaning that it is true - reality may just not be truly probabilistic).

PS: I should probably add that the involved notion of "compatibility" is relative to the test statistic, i.e., if your data look like being generated by a Gamma distribution with mean 15 and variance 1, from the point of view of the t-test (looking at means and variances) this is compatible with a normal(15,1), whereas if you look at things from the point of view of, say, a Shapiro-Wilk test for normality, this will turn out to be incompatible with normality if your sample size is large enough.