# Why aren't type II errors emphasized as much in statistical literature?

I have seen many cases where type I errors are accounted for (denoted by an alpha value) in various research articles. I have found it rare that a researcher will take into consideration the power, or the type II error.

Type II errors can be a big deal right? We have accidentally rejected the alternative hypothesis when it was actually false. Why are alpha values emphasized so much instead of beta values?

When I took first year statistics, I never was taught beta—only alpha. I feel that these two errors should be treated equally. Yet, only alpha seems to be emphasized.

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+1 The reason is that traditionally, the Type I error (aka, $\alpha$ or the significance level) is fixed first, and then the test is constructed such as to minimize the Type II error (equivalently, such as to maximize the power). A helpful article on wikipedia to understand the issue is the one on Uniformly Most Powerful (UMP) tests, en.wikipedia.org/wiki/Uniformly_most_powerful_test – Jeremias K Feb 28 at 18:03
Interesting! Thanks! – sponge_knight Feb 28 at 18:04
You are wrong about "we have accepted the null hypothesis" -- we never accept it. We either "reject null hyp", or "fail to reject null hyp", but never accept null hyp! – caveman Feb 28 at 18:46
blasted - that skimmed past me. Thanks for pointing that out. – sponge_knight Feb 28 at 18:52
Be careful not to confuse your own experience with the entire field of statistical literature; you can hardly infer the content of material you haven't read. – Glen_b Feb 28 at 23:05

This is a good question. Let me begin with a couple of clarifications:

I think you are (unfortunately) right that less attention is paid to power and type II errors. While I think the situation is improving in biomedical research (e.g., funding agencies and IRBs often reqire power analyses now), I think there are a couple of reasons for this:

1. I think power is harder for people to understand than simple significance. (This is in part because it depends on a lot of unknowns—notably the effect size, but there are others as well).
2. Most sciences (i.e., other than physics and chemistry) are not well mathematized. As a result, it is very hard for researchers to know what the effect size 'should' be given their theory (other than just $\ne0$).
3. Scientists have traditionally assumed that type I errors are worse than type II errors.
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As always, enlightening - especially for the non-mathematized :-) ... I love this wording... I wonder if you could expand a bit on the third point... Is there any basis for this bias. I know it's true, but why do you think this is the case... Is it because it's about the trophy of the p-value, and nothing else matters? – Antoni Parellada Mar 6 at 0:47