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To calculate the **sensitivity** of the pregnancy test, we need to follow this formula:

\[
\text{sensitivity} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}} \times 100
\]

From the table:

- **True Positives**: 170 (Pregnancy Test is Positive, and the Woman is Pregnant)
- **False Negatives**: 34 (Pregnancy Test is Negative, but the Woman is Pregnant)

Now, applying the values:

\[
\text{sensitivity} = \frac{170}{170 + 34} \times 100 = \frac{170}{204} \times 100 \approx 83.3
\]

So, the sensitivity of the test is **83.3%** (rounded to the nearest tenth).

Would you like further clarification or details on this? Here are a few related questions for deeper understanding:

1. How is specificity different from sensitivity in diagnostic tests?
2. What is the significance of having a high sensitivity in a medical test?
3. How does the number of false negatives affect the sensitivity?
4. How can this sensitivity value be interpreted in real-world testing scenarios?
5. What are the potential consequences of a test with low sensitivity?

**Tip:** Sensitivity measures how effectively a test identifies true positives and is crucial for minimizing missed cases, especially in conditions where it's important to detect every positive case.

What is the sensitivity of this pregnancy test, based on the collected data? Round to the nearest tenth of a percent. Do not write %.

Learn how to calculate the sensitivity of a pregnancy test using real-world data. This example demonstrates the application of statistics in determining the test's accuracy, using the formula: Sensitivity = (True Positives) / (True Positives + False Negatives) × 100. Suitable for high school students in Grades 11-12.

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