![]() Type I error: This results when a true null hypothesis is rejected. Suppose that the null hypothesis, H 0, is: It’s a Boy Genetic Labs has no effect on gender outcome. It’s a Boy Genetic Labs claim to be able to increase the likelihood that a pregnancy will result in a boy being born. ![]() The following are examples of Type I and Type II errors. Increasing the sample size can increase the Power of the Test. Ideally, we want a high power that is as close to one as possible. Α and β should be as small as possible because they are probabilities of errors. Β = probability of a Type II error = P(Type II error) = probability of not rejecting the null hypothesis when the null hypothesis is false. Α = probability of a Type I error = P(Type I error) = probability of rejecting the null hypothesis when the null hypothesis is true. ![]() The Greek letters α and β represent the probabilities. The decision is to reject H 0 when H 0 is false ( correct decision whose probability is called the Power of the Test).Įach of the errors occurs with a particular probability.The decision is not to reject H 0 when, in fact, H 0 is false (incorrect decision known as a Type II error).The decision is to reject H 0 when H 0 is true (incorrect decision known as a Type I error).The decision is not to reject H 0 when H 0 is true (correct decision).The four possible outcomes in the table are: ![]() The outcomes are summarized in the following table: ACTION When you perform a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis H 0 and the decision to reject or not. Outcomes and the Type I and Type II Errors
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |