Type I and Type II Errors

Two types of error are possible from a hypothesis test: Type I and Type II errors.

• Type I error, also known as a “false positive”, is the error of rejecting a null hypothesis when it is actually true. The probability of making a type I error is alpha $latex \alpha$, which is called the significance level that you set for your hypothesis test. $latex \alpha$ of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis. 
• Type II error, also known as a “false negative”, is the failure to reject a false null hypothesis. The probability of making a type II error is beta $latex \beta$, which is related to the power of the test, which equals $latex 1 – \beta$. The power is the probability of rejecting the null hypothesis when it is false. Note that a type II error might not so much an error when the sample size is too small. You need to ensure the sample size is large enough to detect the effect.