Type I and Type II Errors

A Type I Error is rejecting the null hypothesis--H₀--when it is actually true. The significance level is the probability of a Type I Error.

A Type II Error is failing to reject the H₀ when it is actually false. The power of test is 1 - Probabilty of a Type II Error.

There is a tradeoff for trying to minimize the chance of one of these errors. For a given sample size, decreasing the probability of a Type I Error increases the probability of Type II Error and vice versa.