Criminal investigators use biometric matching for fingerprint recognition, facial recognition, and iris recognition. When matching fingerprints, each subject is given a mean match score for how well their fingerprint matches the given fingerprint, based on different criteria. There are six criteria used to calculate the match score. The mean match score used by a particular police department is 80. If the police department finds a higher match score than this number, they consider the person a fingerprint match and a suspect in the crime. The null hypothesis is that the mean match score is 80. The alternative hypothesis is that the mean match score is greater than 80. Is the following a Type I error or a Type II error or neither? The test shows that the mean match score is more than 80 when the person does not actually have a fingerprint match.

Answer:This is an incomplete question.Step-by-step explanation:When we are "testing an hypothesis" there is a null hypothesis (H0) and and alternative hypothesis (Ha). We usually select the null hypothesis as the one we are intending to reject. For example in the present case the null hypothesis is H0= the mean match score is 80.And the alternative hypothesis:Ha= the mean match score is greater than 80.So, the above means that if H0 is true, the person will not be considered as a suspect of a crime, but is H0 is false and Ha is true, the person will be considered as a suspect.When we are testing an hypothesis there different kind of erros that we can commit:- Type I error: is the error that takes place when we reject H0 but it is actually true.- Type II error: is the error associated to the situation in which we do not reject H0 when it is actually false.- There are no error when we accept H0 and it is effectively true or when we reject H0 and it is false.As in the statement there is no "following sentence" to decide if there is a Type I eror, Type II eror or neither, we only can explain as above.