Loading Type I and Type II Errors…
Quick guide: plain English, key facts and method
In plain English: A Type I error is a false positive. A Type II error is a false negative.
Key facts
- Type I: rejecting the null hypothesis when it is actually true. This is more likely with a lenient level such as p ≤ 0.10.
- Type II: accepting the null hypothesis when it is actually false. This is more likely with a strict level such as p ≤ 0.01.
- p ≤ 0.05 balances the two risks.
- A stricter level is used when a false positive would be serious, for example when testing a new drug.
How to do it
- Look at the significance level and at what the researcher decided.
- Lenient level and a significant result: think Type I.
- Strict level and a non-significant result: think Type II.