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. 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 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 someone is pregnant when they are not, or the conviction of an innocent person. A test result that indicates that a person has a specific disease or condition when the person actually does not have the disease or condition. 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. 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. Here are some examples of false positives and false negatives : Quality Control: a false positive is when a good quality item gets rejected, and a false negative is when a poor quality item gets accepted. (A positive result means there IS a defect.) 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 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 definition: 1. a result of a scientific test that appears to show something exists or is present, when this is…. Learn more.