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  2. False positives and false negatives - Wikipedia

    en.wikipedia.org/wiki/False_positives_and_false...

    Increasing the specificity of the test lowers the probability of type I errors, but may raise the probability of type II errors (false negatives that reject the alternative hypothesis when it is true). [a] Complementarily, the false negative rate (FNR) is the proportion of positives which yield negative test outcomes with the test, i.e., the ...

  3. Sensitivity and specificity - Wikipedia

    en.wikipedia.org/wiki/Sensitivity_and_specificity

    Specificity (true negative rate) is the probability of a negative test result, conditioned on the individual truly being negative. If the true status of the condition cannot be known, sensitivity and specificity can be defined relative to a "gold standard test" which is assumed correct.

  4. Confusion matrix - Wikipedia

    en.wikipedia.org/wiki/Confusion_matrix

    In predictive analytics, a table of confusion (sometimes also called a confusion matrix) is a table with two rows and two columns that reports the number of true positives, false negatives, false positives, and true negatives. This allows more detailed analysis than simply observing the proportion of correct classifications (accuracy).

  5. What Really Causes a False Positive COVID-19 Test? Experts ...

    www.aol.com/false-positive-covid-19-test...

    In the most basic sense, there are four possible outcomes for a COVID-19 test, whether it’s molecular PCR or rapid antigen: true positive, true negative, false positive, and false negative ...

  6. Type I and type II errors - Wikipedia

    en.wikipedia.org/wiki/Type_I_and_type_II_errors

    In statistical hypothesis testing, a type I error, or a false positive, is the rejection of the null hypothesis when it is actually true. A type II error, or a false negative, is the failure to reject a null hypothesis that is actually false. [1] Type I error: an innocent person may be convicted. Type II error: a guilty person may be not convicted.

  7. Positive and negative predictive values - Wikipedia

    en.wikipedia.org/wiki/Positive_and_negative...

    The negative predictive value is defined as: = + = where a "true negative" is the event that the test makes a negative prediction, and the subject has a negative result under the gold standard, and a "false negative" is the event that the test makes a negative prediction, and the subject has a positive result under the gold standard.

  8. Binary classification - Wikipedia

    en.wikipedia.org/wiki/Binary_classification

    Given a classification of a specific data set, there are four basic combinations of actual data category and assigned category: true positives TP (correct positive assignments), true negatives TN (correct negative assignments), false positives FP (incorrect positive assignments), and false negatives FN (incorrect negative assignments).

  9. 105 True or False Questions—Fun Facts To Keep You ... - AOL

    www.aol.com/lifestyle/105-true-false-questions...

    True or False Questions About History. 96. Coca-Cola was the first soft drink in the United States. Answer: False – it was Dr Pepper. 97. Erik the Red was the uncle of famous explorer Leif Erikson.