A false negative refers to:
A test or study result that incorrectly indicates the absence of a condition, effect, or difference when it actually exists.
🔍 In Clinical Testing:
A false negative occurs when:
A diagnostic test fails to detect a disease or abnormality that is actually present.
📌 Example:
A patient has a brain tumor, but the MRI report is interpreted as normal → false negative.
📊 In Research and Statistics:
A false negative is a Type II error, meaning:
The study fails to reject the null hypothesis even though the alternative hypothesis is true.
In practical terms, the study misses a real effect (e.g. a treatment actually works, but the study concludes it doesn't).
🔸 Common causes:
Small sample size
Inadequate study design
High variability
Low statistical power
⚠️ Consequences:
Delay in diagnosis or treatment
Underestimation of treatment benefit
Missed opportunities for clinical or scientific advancement