4. Type I and Type II Errors in Hypothesis Testing

c. explain a test statistic, Type I and Type II errors, a significance level, and how significance levels are used in hypothesis testing;

What is a Type I error? What is a Type II error? - Type I error is when you incorrectly reject the null hypothesis (you say it is false when it is in fact true) and accept the alternate hypothesis. - Type II is the opposite.

Is a Type I or Type II error generally considered worse? Why? The Type I error is worse than a Type II error. This is because a conclusion has been drawn that the null hypothesis is false when, in fact, it is true. On the other hand, a Type II error is only an error in the sense that an opportunity to reject the null hypothesis correctly was lost. It is not an error in the sense that an incorrect conclusion was drawn, since no conclusion is drawn when the null hypothesis is not rejected.