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Sensitivity is the probability of a positive test result given that the person has the disease ($P(+\mid D)$), while specificity is the probability of a negative test result given that the person does not have the disease ($P(-\mid D^c)$). In the model, both are assumed to be $95\%$, but they apply to different subgroups: the sick versus the healthy.

Conditions: The test has $95\%$ sensitivity.; The test has $95\%$ specificity.; The model distinguishes between diseased and non-diseased populations.

Sensitivity is the probability of a positive test result given that the person has the disease ($P(+\mid D)$), while specificity is the probability of a negative test result given that the person does not have the disease ($P(-\mid D^c)$). In the model, both are assumed to be $95\%$, but they apply to different subgroups: the sick versus the healthy.

Conditions: The test has $95\%$ sensitivity.; The test has $95\%$ specificity.; The model distinguishes between diseased and non-diseased populations.

A positive test result does not imply a high probability of illness because the base rate (prevalence) of the disease is very low compared to the healthy population. Even with high sensitivity and specificity, the sheer number of healthy individuals produces a large absolute number of false positives.

Conditions: The disease prevalence is $1\%$.; The test has $95\%$ sensitivity and $95\%$ specificity.; The population is a hypothetical model of 2,000 people.

A positive test result does not imply a high probability of illness because the base rate (prevalence) of the disease is very low compared to the healthy population. Even with high sensitivity and specificity, the sheer number of healthy individuals produces a large absolute number of false positives.

Conditions: The disease prevalence is $1\%$.; The test has $95\%$ sensitivity and $95\%$ specificity.; The population is a hypothetical model of 2,000 people.