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.