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  2. A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present.
    en.wikipedia.org/wiki/False_positives_and_false_n…
    False positive, diterjemahkan bebas sebagai "positif palsu", biasanya disebabkan algoritme suatu program yang menyatakan adanya suatu gejala/sinyal/objek yang sebetulnya tidak ada. Disebut juga dengan false alarm.
    id.wikipedia.org/wiki/False_positive
    In statistical hypothesis testing, a type I error is the mistaken rejection of an actually true null hypothesis (also known as a "false positive" finding or conclusion; example: "an innocent person is convicted"), while a type II error is the failure to reject a null hypothesis that is actually false (also known as a "false negative" finding or conclusion; example: "a guilty person is not convicted").
    en.wikipedia.org/wiki/Type_I_and_type_II_errors
     
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  4. False positives and false negatives - Simple English Wikipedia, …

  5. False Positives and False Negatives - Definition and …

    WebDefinition and explanation. False positives and negatives occur when the outcome of an experiment does not accurately reflect what happened in reality. Suppose you are going through airport security and, being the …

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  6. ‘What do you mean, it was a false positive?’ Making …

    WebSep 8, 2020 · False positive. COVID-19 testing. False negative. Sensitivity. Register now. Understanding terms like sensitivity and specificity can help us make sense of COVID-19 testing, the accuracy of...

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