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. Use this toolkit to recognize false-positive results and help with false-positive investigations. A false-positive test result means that your drug test shows the presence of a medication or substance that you aren't actually taking. For example, a methamphetamine test comes back positive for methamphetamines, even though you haven't taken any. False-positive results can be due to a laboratory error, but the most common reason for a false-positive methamphetamine test is other medications. 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 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. 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 acceptance of a false null hypothesis. [1] An analysis commits a Type I error when some baseline assumption is incorrectly rejected because of new, misleading information. False Positive: Directed by John Lee. With Ilana Glazer, Justin Theroux, Gretchen Mol, Sabina Gadecki. As if getting pregnant weren't complicated enough, Lucy sets out to uncover the unsettling truth about her fertility doctor. 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.” Two important types of errors are: False Positive (FP): Incorrectly classifying a negative sample as positive. False Negative (FN): Incorrectly classifying a positive sample as negative. Both types of errors significantly impact model performance, especially in applications such as fraud detection, medical diagnosis and spam filtering. The FDA's investigation into the source of the cyclosporiasis outbreak is causing confusion as companies and consumers alike navigate new updates.