Alpha levels are used in hypothesis tests. The significance level α is the probability of making the wrong decision when the null hypothesis is true.

## Alpha Levels: Type I and Type II errors

In hypothesis tests, two errors are possible, Type I errors and Type II errors.

**Type I error**: Supporting the alternate hypothesis when the null hypothesis is true.

**Type II error**: Not supporting the alternate hypothesis when the alternate hypothesis is true.

In an example of a courtroom, let’s say that the null hypothesis is that a man is innocent and the alternate hypothesis is that he is guilty. if you convict an innocent man (Type I error), you support the alternate hypothesis (that he is guilty). A type II error would be letting a guilty man go free.

An **alpha level** is the probability of a type I error, or you reject the null hypothesis when it is true. A related term, beta, is the opposite; the probability of rejecting the alternate hypothesis when it is true.

This graph shows the rejection region to the far right.

## How do I Pick an Alpha Level?

Alpha levels can be controlled by you and are related to **confidence intervals**. To get the alpha level, subtract your confidence interval from 1. For example, if you want to be 95 percent confident that your analysis is correct, the alpha level would be 1 – .95 = 5 percent, assuming you had a one tailed test. For two-tailed tests, divide the alpha level by 2. In this example, the two tailed alpha would be .50/2 = 2.5 percent. See: One-tailed test or two? for the difference between a one-tailed test and a two-tailed test.

Picture courtesy of the University of Texas.

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