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Base rate

As explained in Fabulous Adventures in Data Structures and Algorithms

Prevalence is the proportion of a population that has a specified disease or condition at a given time.

A base rate, or prevalence, is the proportion of a population with a specified condition. It supplies the prior probability before additional evidence is known. section 13.1

Table 13.1 A survey of 10,000 respondents organized in a marginal table

Cold: YES

Cold: NO

Total

Sneezed: YES

860

270

1,130

Sneezed: NO

140

8,730

8,870

Total

1,000

9,000

10,000

The survey table shows cold prevalence as 1,000 of 10,000 people, or 10%, alongside sneeze counts. section 13.1

A rare condition and a positive test

Frob Syndrome affects 1 in 30,000 people. Even with a test that has 99.5% sensitivity and specificity, a positive result yields only a 0.7% posterior probability in this example. Test accuracy alone does not determine the chance that a positive result is correct; prevalence matters. section 13.4

How Fabulous Adventures in Data Structures and Algorithms covers it

How deep it goes
Defines prevalence and illustrates its effect with survey counts and a medical-test calculation.
Examples
Uses cold and sneezing survey counts and a rare-syndrome test with 99.5% sensitivity and specificity.
What you can do after
Evaluate how prevalence changes the posterior chance of a condition after a positive test.
How it connects
Connects prevalence to prior probability, conditional probability, Bayes’ theorem, sensitivity, and specificity.

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.