## **Core Concept**
The question assesses understanding of screening test characteristics, specifically **specificity**, which is the test's ability to correctly identify those without the disease (true negatives).

## **Why the Correct Answer is Right**
To calculate specificity, we use the formula: Specificity = TN / (TN + FP), where TN is the number of true negatives and FP is the number of false positives. Given that a positive result is seen in 10% of the healthy population, this implies that 10% are false positives. Also, a negative result is seen in 50% of the non-diseased population, implying that 50% are true negatives. However, to directly calculate specificity from the given data: if 50% of non-diseased have a negative result, and 10% of healthy (non-diseased) have a positive result, then specificity = 50% / (50% + 10%) = 50 / 60 = 5/6 ≈ 0.833 or 83.3%.

## **Why Each Wrong Option is Incorrect**
- **Option A:** This option is incorrect because it does not match our calculated specificity.
- **Option B:** This option suggests a specificity of 90%, which does not align with our calculations based on the provided data.
- **Option D:** This option is incorrect as it suggests a lower specificity than what we calculated.

## **Clinical Pearl / High-Yield Fact**
A key point to remember is that **specificity** refers to the test's ability to correctly identify those without the disease. A high specificity means that a negative result effectively rules out the disease. Understanding the predictive values and test characteristics like sensitivity, specificity, PPV, and NPV is crucial in interpreting screening test results.

## **Correct Answer:** C. 83.3%.