A test that catches 50 of 55 cases
tp = 50fp = 10fn = 5tn = 35
Result: Sensitivity 0.9091 · False negative rate 0.0909
Sensitivity Calculator
Sensitivity — also called recall or the true positive rate — measures how good a test is at detecting the condition it looks for: TP ÷ (TP + FN). It is the primary quality metric for medical screening and detection systems, where missing a real positive is the worst failure. Enter your confusion-matrix counts and get sensitivity plus the false negative rate.
Enter your values and press Calculate — the result appears here.
Worked examples
Perfect detection
tp = 40fp = 8fn = 0tn = 52
Result: Sensitivity 1.00
Frequently asked questions
What does sensitivity measure?
The proportion of actual positives the test detected. A 90.91% sensitive test finds 50 of every 55 real cases and misses the other 5 — those misses are the false negatives.
Is sensitivity the same as recall?
Yes — identical formula (TP ÷ (TP + FN)). Medicine and diagnostics usually say sensitivity; information retrieval and machine learning usually say recall.
Why is sensitivity so important in screening?
In screening, failing to detect a disease is far worse than a false alarm. A highly sensitive test rarely misses real cases, which is why sensitivity is paired with specificity when evaluating medical tests.
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Compute recall (sensitivity) — the share of actual positives the model managed to find.
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Compute specificity — the share of actual negatives the model correctly identified.
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Compute the F1 score — the harmonic mean of precision and recall — from TP, FP, FN, TN.
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Calculate classification accuracy from true/false positives and negatives.
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