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To match each confidence level with its corresponding critical value, we need to understand the relationship between confidence levels and z-scores (critical values) from the standard normal distribution.

Here's how they pair up:

1. **80% confidence level** corresponds to **1.28**
2. **85% confidence level** corresponds to **1.44**
3. **90% confidence level** corresponds to **1.645**
4. **95% confidence level** corresponds to **1.96**
5. **99% confidence level** corresponds to **2.576**
6. **99.9% confidence level** corresponds to **3.29**

So, the matches are:

- **80%** → **1.28**
- **85%** → **1.44**
- **90%** → **1.645**
- **95%** → **1.96**
- **99%** → **2.576**
- **99.9%** → **3.29**

Would you like more details on how these values are calculated or why they're used? Here are five related questions to deepen understanding:

1. How is the critical value determined for a given confidence level?
2. Why do higher confidence levels have higher critical values?
3. What is the importance of the z-score in hypothesis testing?
4. How do confidence intervals change with different sample sizes?
5. What’s the difference between a one-tailed and two-tailed test in terms of critical values?

**Tip:** When working with z-scores, remember that a higher confidence level means a wider interval, providing more certainty that the population parameter falls within the interval.

Match each confidence level with its corresponding critical value.

Learn how to match different confidence levels, such as 80%, 90%, 95%, and 99%, with their corresponding critical values from the standard normal distribution. Understand key statistical concepts and the relationship between confidence levels and Z-scores, suitable for high school students.

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