Okay, let's tackle this question. The user is asking about the specificity of a screening test. The question states that the test was positive in 50% of the diseased population and 10% of the healthy population. Hmm, wait, specificity is the true negative rate, right? So it's the proportion of healthy people who test negative. If the test is positive in 10% of the healthy population, that means 90% of healthy people test negative. So the specificity should be 90% or 0.90.

But wait, let me double-check. The question mentions the test is positive in 50% of diseased. That's the sensitivity, the true positive rate. Sensitivity is about how often the test correctly identifies those with the disease. Specificity, on the other hand, is about correctly identifying those without the disease. The 10% of healthy people who test positive are false positives, so the specificity is 100% - 10% = 90%.

The options aren't provided, but the correct answer here is 90%. For the wrong options, maybe someone might confuse sensitivity with specificity. For example, if someone thought the 50% figure was the specificity, that's a common mistake. Others might miscalculate by adding or subtracting incorrectly. Another mistake could be using the positive rate in diseased (50%) as specificity, which is incorrect. Also, someone might think that the false positive rate is the specificity, but the false positive rate is 10%, so specificity is 1 - false positive rate = 90%.

Clinical pearl: Remember that specificity focuses on the healthy population. It's the ability of the test to correctly identify those without the disease. A high specificity means fewer false positives, which is crucial in screening to avoid unnecessary anxiety and further testing.

**Core Concept**
Specificity measures the proportion of *healthy individuals* correctly identified as negative by a test. It is calculated as **True Negatives / (True Negatives + False Positives)**. This question tests understanding of how test performance metrics relate to disease prevalence and test outcomes.

**Why the Correct Answer is Right**
The test is positive in 10% of the healthy population, meaning **10% are false positives**. Specificity is defined as the proportion of healthy individuals who test negative, calculated as **1 – False Positive Rate**. Thus, specificity = 1 – 0.10 = **0.90 (90%)**. This reflects the test’s ability to correctly rule out disease in non-diseased individuals.

**Why Each Wrong Option is Incorrect**
**Option A:** Likely represents sensitivity (50%), which measures true positives in diseased individuals, not specificity.
**Option B:** May reflect the false positive rate (10%), which is **1 – specificity**, not specificity itself.
**Option C:** Could stem from miscalculating (e.g., adding 50% + 10% = 60%) or confusing predictive values with specificity.

**Clinical Pearl / High-Yield Fact**
**Specificity focuses on healthy individuals** (TP/FP). A test with high specificity (e.g., 95%) has **few false positives**, making it ideal for confirming a diagnosis. Sensitivity

✓ Correct Answer: B. 0.9