Calculate the true negative count from a confusion matrix and classification results.
True negatives are correctly identified negative cases: TN = total_population × (1 − prevalence) × specificity. Here, (1 − prevalence) is the number of truly negative individuals in the population, and specificity is the fraction of those the test correctly identifies as negative. TN is the basis of specificity and negative predictive value, and along with TP, FP, and FN completes the full 2×2 confusion matrix for a given population.
TN = total * (1 - prevalence) * specificity
When the condition being tested for is rare (low prevalence), the vast majority of the population is truly negative, so even a moderately specific test will correctly classify a large absolute number of true negatives.
Negative Predictive Value (NPV) = TN / (TN + FN), so a higher TN count relative to FN increases confidence that a negative test result truly means the condition is absent.
No — specificity itself (TN rate) is a property of the test/model, independent of population size; only the absolute TN count scales with total population, not the underlying rate.