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False positives are more numerous because the healthy population is much larger than the diseased population. Even though the false positive rate is low ($5\%$), applying it to a large base (1,980 people) yields a higher absolute count (99) than applying the high sensitivity rate ($95\%$) to a small base (20 people), which yields 19.

Conditions: Prevalence is $1\%$ (20 diseased, 1,980 healthy).; False positive rate is $5\%$.; Sensitivity is $95\%$.

False positives are more numerous because the healthy population is much larger than the diseased population. Even though the false positive rate is low ($5\%$), applying it to a large base (1,980 people) yields a higher absolute count (99) than applying the high sensitivity rate ($95\%$) to a small base (20 people), which yields 19.

Conditions: Prevalence is $1\%$ (20 diseased, 1,980 healthy).; False positive rate is $5\%$.; Sensitivity is $95\%$.

The model produces 19 true positives and 99 false positives. True positives are calculated from the 20 diseased individuals using the $95\%$ sensitivity, while false positives are calculated from the 1,980 healthy individuals using the $5\%$ false positive rate (derived from $95\%$ specificity).

Conditions: Population size is 2,000.; Prevalence is $1\%$ (20 diseased, 1,980 healthy).; Sensitivity is $95\%$.; Specificity is $95\%$.

The model produces 19 true positives and 99 false positives. True positives are calculated from the 20 diseased individuals using the $95\%$ sensitivity, while false positives are calculated from the 1,980 healthy individuals using the $5\%$ false positive rate (derived from $95\%$ specificity).

Conditions: Population size is 2,000.; Prevalence is $1\%$ (20 diseased, 1,980 healthy).; Sensitivity is $95\%$.; Specificity is $95\%$.

To calculate the probability of having the disease given a positive test result, you must count the number of true positives and divide it by the total number of positive results (true positives plus false positives). In the model, this involves identifying the 19 true positives out of the 118 total positive tests, yielding approximately $16.1\%$.

Conditions: Prevalence is $1\%$ in a population of 2,000.; Sensitivity is $95\%$.; Specificity is $95\%$.

To calculate the probability of having the disease given a positive test result, you must count the number of true positives and divide it by the total number of positive results (true positives plus false positives). In the model, this involves identifying the 19 true positives out of the 118 total positive tests, yielding approximately $16.1\%$.

Conditions: Prevalence is $1\%$ in a population of 2,000.; Sensitivity is $95\%$.; Specificity is $95\%$.