Coming back to the topic of my previous post, about how we must draw distinct conclusions from different hypothesis test procedures, I’d like to show an example of how these confusions might actually arise in practice. The following example comes from Royall’s book (you really should read it), and questions why we settle for a power of only 80%. It’s a question we’ve probably all thought about at some point. Isn’t 80% power just as arbitrary as p-value thresholds? And why should we settle for such a large probability of error before we even start an experiment?

From Royall (1997, pp. 109-110):

**Why is a power of only 0.80 OK?**

We begin with a mild peculiarity — why is it that the Type I error rate α is ordinarily required to be 0.05 or 0.01, but a Type II error rate as large as 0.20 is regularly adopted? This often occurs when the sample size for a clinical trial is being determined. In trials that compare a new treatment to an old one, the ‘null’ hypothesis usually states that the new treatment is not better than the old, while the alternative states that it is. The specific alternative value chosen might be suggested by pilot studies or uncontrolled trials that preceded the experiment that is now being planned, and the sample size is determined [by calculating power] with α = 0.05 and β = 0.20. Why is such a large value of β acceptable? Why the severe asymmetry in favor of α? Sometimes, of course, a Type I error would be much more costly than a Type II error would be (e.g. if the new treatment is much more expensive, or if it entails greater discomfort). But sometimes the opposite is true, and we never see studies proposed with α = 0.20 and β = 0.05. No one is satisfied to report that ‘the new treatment is statistically significantly better than the old (*p* ≤ 0.20)’.

Often the sample-size calculation is first made with β = α = 0.05. But in that case experimenters are usually quite disappointed to see what large values of *n* are required, especially in trials with binomial (success/failure) outcomes. They next set their sights a bit lower, with α = 0.05 and β = 0.10, and find that *n* is still ‘too large’. Finally they settle for α = 0.05 and β = 0.20.

Why do they not adjust α and settle for α = 0.20 and β = 0.05? Why is small α a non-negotiable demand, while small β is only a flexible desideratum? A large α would seem to be scientifically unacceptable, indicating a lack of rigor, while a large β is merely undesirable, an unfortunate but sometimes unavoidable consequence of the fact that observations are expensive or that subjects eligible for the trial are hard to find and recruit. We might have to live with a large β, but good science seems to demand that α be small.

*What is happening is that the formal Neyman-Pearson machinery is being used, but it is being given a rejection-trial interpretation* (Emphasis added). The quantities α and β are not just the respective probabilities of choosing one hypothesis when the other is true; if they were, then calling the first hypothesis H2 and the second H1 would reverse the roles of α and β, and α = 0.20, β = 0.05 would be just as satisfactory for the problem in its new formulation as α = 0.05 and β = 0.20 were in the old one. The asymmetry arises because the quantity α is being used in the dual roles that it plays in rejection trials — it is both the probability of rejecting a hypothesis when that hypothesis is true and the measure of strength of the evidence needed to justify rejection. Good science demands small α because small α is supposed to mean strong evidence. On the other hand, *the Type II error probability β is being interpreted simply as the probability of failing to find strong evidence against H1 when the alternative H2 is true* (Emphasis added. Recall Fisher’s quote about the impossibility of making Type II errors since we never accept the null.) … When observations are expensive or difficult to obtain we might indeed have to live with a large probability of failure to find strong evidence. In fact, when the expense or difficulty is extreme, we often decide not to do the experiment at all, thereby accpeting values of α = 0 and β = [1].

**— End excerpt.**

So there we have our confusion, which I alluded to in the previous post. We are imposing rejection-trial reasoning onto the Neyman-Pearson decision framework. We accept a huge β because we interpret our results as a mere *failure (to produce strong enough evidence)* *to reject the null*, when really our results imply *a decision to accept the ‘null’.* Remember, with NP we are always forced to choose between two hypotheses — we can never abstain from this choice because the respective rejection regions for H1 and H2 encompass the entire sample space by definition; that is, any result obtained must fall into one of the rejection regions we’ve defined. We can adjust either α or β (before starting the experiment) as we see fit, based on the relative costs of these errors. Since neither hypothesis is inherently special, adjusting α is as justified as adjusting β and neither has any bearing on the strength of evidence from our experiment.

And surely it doesn’t matter which hypothesis is defined as the null, because then we would just switch the respective α and β — that is, H1 and H2 can be reversed without any penalty in the NP framework. Who cares which hypothesis gets the label 1 or 2?

**But** **imagine the outrage** (and snarky blog posts) if we tried swapping out the null hypothesis with our pet hypothesis in a rejection trial. Would anybody buy it if we tried to accept our pet hypothesis simply based on a failure to reject it? Of course not, because that would be absurd. Failing to find strong evidence *against* a single hypothesis has no logical implication that we have found evidence *for* that hypothesis. Fisher was right about this one. And this is yet another reason NP procedures and rejection trials don’t mix.

However, when we are using concepts of power and Type II errors, we are working with NP procedures which are completely symmetrical and have no concept of strength of *evidence *per se. Failure to reject the null hypothesis has the exact same meaning as accepting the null hypothesis — they are simply different ways to say the same thing. If what you want is to measure evidence, fine; I think we should be measuring evidence in any case. But then you don’t have a relevant concept of power, as Fisher has reiterated time and time again. If you want to use power to help plan experiments (as seems to be recommended just about everywhere you look) then you must cast aside your intuitions about interpreting observations from that experiment as evidence. **You must reject the rejection trial and reject notions of statistical evidence. **

Or don’t, but then you’re swimming in a sea of confusion.

References

Royall, R. (1997). *Statistical evidence: a likelihood paradigm* (Vol. 71). CRC press.

Why 80% power is a good question. Of course, more is better and 95% may be considered best because it equates the type-I and type-II error rates. As noted in the post, the reason for chosen relatively low power of just 80% is that resources are limited and more power costs more resources. So, why 80% and not 50% or 30%. The reason is that power less than 50% means that studies can produce significant results ONLY with the help of sampling error. The true effect size alone cannot produce a significant result. With 50% power, a study has to match the true effect size exactly to produce a just significant result (p = .05). With 80% power there is some room for sampling error to work against a researcher and to still get a significant result. As a result, sampling error can also average out in a meta-analysis and there is only a small bias when non-significant results are missing. There are other good reasons for 80% power and researchers should aim for 80% power in their studies. The R-Index can reveal researchers typical power in their studies. Often it is much lower than 80%. www-r-index.org

But why is it never done the other way, where the alpha level is adjusted?

because nobody realized how low power is to achieve .05. Once the real power is apparent, it makes more sense to increase alpha and report an honest type-I error rate.