There is always a tradeoff between controlling the level of false positives and false negatives. This is because we as scientists set the criterion for statistical significance. A false positive is where you receive a positive result for a test, when you should have received a negative results. It’s sometimes called a “ false alarm ” or “false positive error.” Type I error, or a false positive, is the incorrect rejection of a true null hypothesis in statistical hypothesis testing. A type II error, or a false negative, is the incorrect failure to reject a false null hypothesis. [1] A false positive —a test result indicative of disease that isn't actually present—can trigger a chain reaction of worry, further tests, and even unnecessary treatment. A false positive error, or false positive, is a result that indicates a given condition exists when it objectively does not. For example, a pregnancy test which indicates a woman is pregnant when she is not, or the conviction of an innocent person. False positives can result in Type I errors, meaning the null hypothesis is incorrectly rejected. In situations where false positives are frequent, the model may become overly sensitive, incorrectly identifying normal situations as problematic. A false positive is a test result that says something is present when it actually isn’t. The test detects a condition, substance, or disease that the person doesn’t truly have. It’s the medical or scientific equivalent of a fire alarm going off when there’s no fire. False positive - the individual does not have the disease or illness, but the test results are positive. When a test’s specificity is high, it is more likely to give a true negative result and correctly identify that the disease or illness is not present. A false positive occurs when a test incorrectly indicates a condition is present when it is not. A false negative occurs when a test incorrectly indicates a condition is absent when it actually is present. A test yields a positive result, but in fact the condition is not present (e.g. your test says you have COVID, but you don't).