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Recall

As explained in Fabulous Adventures in Data Structures and Algorithms

Test sensitivity is the conditional probability that a medical test is positive when the tested condition is present.

Recall, also called test sensitivity, measures how often a medical test returns positive when the tested condition is present. A test with 99.5% sensitivity detects 995 of 1,000 genuine cases. section 13.4

Sensitivity describes positive-case accuracy, not the probability that a positive result means the condition is present. That interpretation also depends on the prior probability and the test’s specificity. section 13.4

A highly sensitive test can still mislead

For Frob Syndrome, the prior is 1 case in 30,000. With 99.5% sensitivity and specificity, 199 genuinely affected people test positive, but 29,999 unaffected people also test positive. Among positive results, the posterior odds are 199 to 29,999, or about 0.7%. section 13.4

How Fabulous Adventures in Data Structures and Algorithms covers it

How deep it goes
Defines sensitivity numerically and contrasts it with specificity, then stops short of treating sensitivity as posterior accuracy.
Examples
Uses Frob Syndrome, a 99.5%-accurate test, six-million-person counts, and a C# joint distribution.
What it assumes
Assumes conditional probability and Bayesian reasoning while defining sensitivity and specificity within the testing example.
What you can do after
Evaluate medical test results by separating sensitivity from posterior belief and accounting for false positives.
How it connects
Connects test sensitivity to specificity, conditional probability, prior probability, likelihood, posterior probability, and Bayes’ theorem.
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This explainer was generated by Manning's knowledge graph from the text of Fabulous Adventures in Data Structures and Algorithms. The section links point to the parts of the book it draws on.