Suppose the null hypothesis, H0, is: the mean age of the horses on a ranch is 6 years. What is the Type II error in this scenario?

The type II error in statistical hypothesis testing, refers to the non-rejection of a false null hypothenuse, because it means there was an error in the data analysis or the sample is not representative enough.

In this case, we would have a type II error if there is the mean age of the horses is not 6 years, and the researcher gets a result where he or she cannot reject this false null hypothesis.

In other words, the analysis doesn't match the phenomenom.

Additionally, type II error is know as false negative.

We would have a Type II error if we conclude that the mean age of the horses on the ranch is not significantly different from 6, when actually it is.

Step-by-step explanation:

When conducting the hypothesis test, the Type II error occurs when, based on the sample of horses we have, we are not able to refuse the null hypothesis that the mean age of the horses is 6 years, but at population level it is different from 6.