α

Definition

α (alpha) is the significance level in hypothesis testing, representing the probability of rejecting the null hypothesis when it is actually true. Commonly set at 0.05, it indicates a 5% risk of Type I error.

5 Must Know Facts For Your Next Test

1. Alpha (α) represents the threshold for statistical significance in hypothesis testing.
2. A common alpha value used in many tests is 0.05, but it can vary depending on the study's requirements.
3. Choosing a smaller alpha reduces the risk of Type I error but increases the risk of Type II error.
4. If the p-value is less than or equal to α, you reject the null hypothesis.
5. Alpha is a predetermined value set before conducting the test and is not influenced by sample data.

Review Questions

• What does α represent in hypothesis testing?
• How does changing α affect Type I and Type II errors?
• Why must α be set before conducting a statistical test?

Related terms

Type I Error: The mistake of rejecting a true null hypothesis; its probability is denoted by α.

Type II Error: The mistake of failing to reject a false null hypothesis; its probability is denoted by β.

$p$-value: $p$-value measures the strength of evidence against the null hypothesis; if $p \leq \alpha$, we reject the null hypothesis.