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It looks like you uploaded an image related to a statistics problem asking you to perform a Z-test and find the *p*-value. Here's a breakdown of the problem and steps involved:

1. **Z-Test Formula**: 
   \[
   Z = \frac{\bar{X} - \mu}{\frac{\sigma}{\sqrt{n}}}
   \]
   Where:
   - \(\bar{X}\) is the sample mean.
   - \(\mu\) is the population mean.
   - \(\sigma\) is the population standard deviation.
   - \(n\) is the sample size.

2. **P-Value**: 
   The *p*-value is the probability that the test statistic (Z) falls within the observed distribution under the null hypothesis. The area under the curve to the left of the calculated Z-score gives the *p*-value.

3. **Steps on the Image**:
   - **Step 1**: Choose between a one-tailed or two-tailed test. A two-tailed test is chosen in your case.
   - **Step 2**: Input the Z-score (rounded to 3 decimal places).
   - **Step 3**: The shaded region under the curve represents the *p*-value.
   - **Step 4**: Input the calculated *p*-value (rounded to 3 decimal places).

Let me know the Z-score or any other values you have, and I can help you calculate the *p*-value. Alternatively, I can guide you through the process of solving it.

Would you like details on how to calculate this or have any questions?

Here are 5 related questions for further exploration:
1. What is the difference between one-tailed and two-tailed tests?
2. How is the Z-score related to confidence intervals?
3. Why is the normal distribution used in Z-tests?
4. What is the significance level (\(\alpha\)) and how does it relate to the *p*-value?
5. How do we interpret the *p*-value in hypothesis testing?

**Tip**: Always check whether your test should be one-tailed or two-tailed. This impacts the area you calculate under the curve and the corresponding *p*-value.

Perform a Z-test and find the p-value using the given Z-test formula and normal distribution chart.

Learn how to perform a Z-test and calculate the p-value using the formula Z = (X̄ - μ) / (σ / √n). The problem involves understanding normal distribution and hypothesis testing. Suitable for college-level statistics students.

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