This article discusses the importance of correctly understanding and applying sensitivity, specificity, and predictive values in screening tests. It highlights the potential for confusion between these metrics and their implications for clinical decision-making. Sensitivity refers to a test's ability to correctly identify those with a condition, while specificity refers to its ability to correctly identify those without the condition. Predictive values, such as positive and negative predictive values, indicate the probability that a person actually has or does not have a condition based on the test result. The article emphasizes that sensitivity and specificity are typically used to describe a screening test's attributes relative to a reference standard, while predictive values are more appropriate for actual screening contexts. However, high sensitivity and specificity can be useful for screening decisions about individual people. It also notes that predictive values do not always need to be high and can be adjusted by modifying the sensitivity and specificity of screening tests. The article stresses the importance of providing complete information about all four metrics—sensitivity, specificity, and predictive values—when describing screening tests. This includes how these metrics were derived and their appropriate interpretations. It also highlights the need for researchers and clinicians to avoid confusion between the inverse of these metrics, as this can lead to misinterpretation and incorrect clinical decisions. The article concludes that while sensitivity and specificity are important, predictive values are more relevant when people are being screened. It also emphasizes the importance of careful clinical deliberation when determining predictive values, as they may be used in a reverse process that adjusts sensitivity and specificity values. Overall, the article underscores the importance of accurate understanding and application of these metrics in research and practice to ensure the best outcomes for individuals and the healthcare system.This article discusses the importance of correctly understanding and applying sensitivity, specificity, and predictive values in screening tests. It highlights the potential for confusion between these metrics and their implications for clinical decision-making. Sensitivity refers to a test's ability to correctly identify those with a condition, while specificity refers to its ability to correctly identify those without the condition. Predictive values, such as positive and negative predictive values, indicate the probability that a person actually has or does not have a condition based on the test result. The article emphasizes that sensitivity and specificity are typically used to describe a screening test's attributes relative to a reference standard, while predictive values are more appropriate for actual screening contexts. However, high sensitivity and specificity can be useful for screening decisions about individual people. It also notes that predictive values do not always need to be high and can be adjusted by modifying the sensitivity and specificity of screening tests. The article stresses the importance of providing complete information about all four metrics—sensitivity, specificity, and predictive values—when describing screening tests. This includes how these metrics were derived and their appropriate interpretations. It also highlights the need for researchers and clinicians to avoid confusion between the inverse of these metrics, as this can lead to misinterpretation and incorrect clinical decisions. The article concludes that while sensitivity and specificity are important, predictive values are more relevant when people are being screened. It also emphasizes the importance of careful clinical deliberation when determining predictive values, as they may be used in a reverse process that adjusts sensitivity and specificity values. Overall, the article underscores the importance of accurate understanding and application of these metrics in research and practice to ensure the best outcomes for individuals and the healthcare system.