<P> In statistics, a null hypothesis is a statement that one seeks to nullify with evidence to the contrary . Most commonly it is a statement that the phenomenon being studied produces no effect or makes no difference . An example of a null hypothesis is the statement "This diet has no effect on people's weight ." Usually, an experimenter frames a null hypothesis with the intent of rejecting it: that is, intending to run an experiment which produces data that shows that the phenomenon under study does make a difference . In some cases there is a specific alternative hypothesis that is opposed to the null hypothesis, in other cases the alternative hypothesis is not explicitly stated, or is simply "the null hypothesis is false"--in either event, this is a binary judgment, but the interpretation differs and is a matter of significant dispute in statistics . </P> <P> A type I error (or error of the first kind) is the incorrect rejection of a true null hypothesis . Usually a type I error leads one to conclude that a supposed effect or relationship exists when in fact it doesn't . Examples of type I errors include a test that shows a patient to have a disease when in fact the patient does not have the disease, a fire alarm going on indicating a fire when in fact there is no fire, or an experiment indicating that a medical treatment should cure a disease when in fact it does not . </P> <P> A type II error (or error of the second kind) is the failure to reject a false null hypothesis . Examples of type II errors would be a blood test failing to detect the disease it was designed to detect, in a patient who really has the disease; a fire breaking out and the fire alarm does not ring; or a clinical trial of a medical treatment failing to show that the treatment works when really it does . </P> <P> In terms of false positives and false negatives, a positive result corresponds to rejecting the null hypothesis, while a negative result corresponds to failing to reject the null hypothesis; "false" means the conclusion drawn is incorrect . Thus a type I error is a false positive, and a type II error is a false negative . </P>

Describe type i and type ii errors of a mammography in terms of the diagnosis