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Background and Concept: False Alarm and Missed Alert are better understood with the two types of errors that are possible in statistical Hypothesis testing. Dealing with them with reference to test of hypotheses will provide more insights than otherwise. Any hypothesis test is begun with the assumption that the null hypothesis is correct. Null hypothesis is the default position and corresponds to the idea that "one is innocent until proven guilty". False alarm or Type I errors or False Positives (α): They happen when we reject a true null hypothesis. Missed alert or Type II errors or False Negatives (β): They happen when we accept (fail to reject) a false null hypothesis. Which error will you prefer over the other? The answer to this question depends on the problem and the worst that could happen if either a Type1 or Type 2 error was committed. Example 1: Person accused of Murder awaiting Death Sentence. Null Hypothesis: Person did not commit murder. Type 1 error: Person did not commit murder but pronounced guilty. (Rejected true Null Hypothesis) Type 2 error: Person committed murder but pronounce Not guilty. (Accepted false Null Hypothesis) In this example, though Type 2 error is not favorable to society, but hanging an innocent person is far worse. So Type2 error or a Missed alert is preferable. Example 2: Person being screened for a disease to prescribe further tests. Null Hypothesis: Person does not have the disease. Type 1 error: Person does not have the disease but recommended for further tests. (Rejected true Null Hypothesis) Type 2 error: Person has the disease but recommended for no further tests. (Accepted false Null Hypothesis) In this example, Type 1 error might cause the patient to undergo further tests but might finally reveal that he does not have the disease. A type 2 error would prevent a legitimate patient from undergoing further tests. But a legitimate patient can re-do the test if the symptoms persist, and it is fine for a person to do some further tests even if he does not have the disease. So Type1 error or False alarm is preferable. Example 3: Person being screened for a disease (presence of which has a good rate of survival and normal life) to prescribe a delicate specialised surgery that has poor success rate. Null Hypothesis: Person does not have the disease. Type 1 error: Person does not have the disease but recommended for surgery. (Rejected true Null Hypothesis) Type 2 error: Person has the disease but not recommended for surgery. (Accepted false Null Hypothesis) In this example, Type 2 error might cause the legitimate patient to not have the surgery which is bad, but it is much worse to have a person without the disease undergo the delicate critical surgery. The legitimate patient may re-do the tests, if he still feels the symptoms of the disease and may be re-diagnosed to undergo the surgery. In this case, a Type2 error or a Missed alert is preferable.