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Positive and negative predictive values
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<p>The positive and negative predictive values (PPV and NPV respectively) are the proportions of positive and negative results in <a href="/facts/Predictive_value_of_tests/XKBg6uDg">statistics</a> and <a href="/facts/Diagnostic_test/rRgnuQSD">diagnostic tests</a> that are <a href="/facts/True_positive/jkC44hML">true positive</a> and <a href="/facts/True_negative/jkC44hML">true negative</a> results, respectively. 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 (as <a href="/facts/True_positive_rate/pzc9qfdz">true positive rate</a> and <a href="/facts/True_negative_rate/pzc9qfdz">true negative rate</a> are); they depend also on the <a href="/facts/Prevalence/6UTuTGTH">prevalence</a>. Both PPV and NPV can be derived using <a href="/facts/Bayes%27_theorem/wQqFB2Dt">Bayes' theorem</a>. </p><p>Although sometimes used synonymously, a <i>positive predictive value</i> generally refers to what is established by control groups, while a <a href="/facts/Pre-_and_post-test_probability/jBJpIreq">post-test probability</a> refers to a probability for an individual. Still, if the individual's <a href="/facts/Pre-test_probability/jBJpIreq">pre-test probability</a> 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. </p><p>In <a href="/facts/Information_retrieval/MFSmpeNw">information retrieval</a>, the PPV statistic is often called the <a href="/facts/Precision_and_recall/Jnmmpl8H">precision</a>. </p>