To calculate the p-value for the given test statistic, we can use the standard normal distribution. The p-value corresponds to the probability of observing a test statistic as extreme as the one calculated, assuming the null hypothesis is true.

Given:
- Test statistic \( Z = -0.899 \)

Since this is a one-tailed test (we are testing if the proportion is **less than** 0.63), the p-value is the probability that a standard normal variable is less than \(-0.899\).

We can find the p-value by looking up the cumulative probability for \( Z = -0.899 \) in the standard normal distribution table or using a calculator.

The cumulative probability for \( Z = -0.899 \) is approximately 0.1841.

So, the p-value is:

\[
\text{p-value} = 0.1841
\]

This means that the p-value is approximately **0.184**.

Would you like details or have any questions? Here are some related questions you might consider:

1. How do you interpret this p-value in the context of the hypothesis test?
2. What does a higher or lower p-value indicate in hypothesis testing?
3. What conclusion would you draw at a significance level of 0.02?
4. How does the choice of significance level affect the outcome of the test?
5. What are the implications if the p-value were much smaller or larger?

**Tip:** When the p-value is greater than the significance level, you do not reject the null hypothesis. This means there isn't sufficient evidence to support the alternative hypothesis.

Learn how to calculate the p-value for hypothesis testing using the standard normal distribution. This problem covers key concepts in hypothesis testing and statistical inference, including the interpretation of p-values and their significance levels.

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