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The **positive and negative predictive values** (**PPV** and **NPV** respectively) are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true negative results. The PPV and NPV describe the performance of a diagnostic test or other statistical measure. A high result can be interpreted as indicating the accuracy of such a statistic. The PPV and NPV are not intrinsic to the test; they depend also on the prevalence. The PPV can be derived using Bayes' theorem.

Although sometimes used synonymously, a *positive predictive value* generally refers to what is established by control groups, while a post-test probability refers to a probability for an individual. Still, if the individual's pre-test probability of the target condition is the same as the prevalence in the control group used to establish the positive predictive value, the two are numerically equal.

In information retrieval, the PPV statistic is often called the precision.

This page contains text from Wikipedia, the Free Encyclopedia - https://wn.com/Positive_and_negative_predictive_values

**Predictive value of tests** is the probability of a target condition (for example a disease) given by the result of a test, often in regard to medical tests.

A conversion of continuous values into binary values can be performed, such as designating a pregnancy test as "positive" above a certain cutoff value, but this confers a loss of information and generally results in less accurate predictive values.

This page contains text from Wikipedia, the Free Encyclopedia - https://wn.com/Predictive_value_of_tests

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