Condition | Totals | ||
Absent | Present | ||
Test Positive | |||
Test Negative | |||
Totals |
Estimated Value | 95% Confidence Interval | ||
Lower Limit | Upper Limit | ||
Prevalence | |||
Sensitivity | |||
Specificity | |||
For any particular test result, the probability that it will be: | |||
Positive | |||
Negative | |||
For any particular positive test result, the probability that it is: | |||
True Positive (Positive Predictive Value) | |||
False Positive | |||
For any particular negative test result, the probability that it is: | |||
True Negative (Negative Predictive Value) | |||
False Negative | |||
likelihood Ratios: [C] = conventional [W] = weighted by prevalence [definitions] | |||
Positive [C] | |||
Negative [C] | |||
Positive [W] | |||
Negative [W] | |||
The entry 'NaN' in any of the above cells means that the calculation cannot be performed because the values entered include one or more instances of zero. Technical note on calculation of confidence intervals. |
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Newcombe, Robert G. "Two-Sided Confidence Intervals for the Single Proportion: Comparison of Seven Methods," Statistics in Medicine, 17, 857-872 (1998).Return
Wilson, E. B. "Probable Inference, the Law of Succession, and Statistical Inference," Journal of the American Statistical Association, 22, 209-212 (1927).
= |
conditional probability of positive test result if the condition is present conditional probability of positive test result if the condition is absent | = |
sensitivity
1-specificity |
= |
conditional probability of negative test result if the condition is present conditional probability of negative test result if the condition is absent | = |
1-sensitivity
specificity |
= |
probability that a positive test result is a true positive probability that a positive test result is a false positive | = |
(prevalence)(sensitivity)
(1-prevalence)(1-specificity) |
= |
probability of false negative result
probability of true negative result | = |
(prevalence)(1-sensitivity)
(1-prevalence)(specificity) |