All tests are imperfect: Accounting for false positives and false negatives using Bayesian statistics.

Area of Science:

• Statistics
• Biostatistics
• Medical Testing

Background:

• Binary outcome tests are widely used to determine a binary state.
• These tests are imperfect, with inherent risks of false positives and false negatives.
• Direct interpretation of imperfect test results is unreliable without considering uncertainty.

Purpose of the Study:

• To discuss statistical methods for incorporating uncertainty from imperfect tests.
• To address two scenarios: inference about a subject and inference about a population.
• To demonstrate how to make imperfect test results informative.

Main Methods:

• Statistical analysis of test error rates (false positive and false negative).
• Incorporating prior knowledge and test uncertainty into interpretation.
• Application of methods to scenarios of individual and population-level inference.

Main Results:

• Imperfect test results require statistical adjustment to be informative.
• Methods differ based on whether the inference is for a single subject or a population.
• Proper interpretation changes how tests are planned and conducted.

Conclusions:

• Statistical considerations are crucial for accurate interpretation of imperfect tests.
• Accounting for uncertainty transforms raw test results into meaningful information.
• The presented methods are broadly applicable across various testing domains.