You wish to test the following claim (𝐻𝑎) at a significance level of 𝛼=0.05.
      𝐻𝑜:𝑝1=𝑝2
      𝐻𝑎:𝑝1≠𝑝2

The 1st population's sample has 539 successes and a sample size = 754.
The 2nd population's sample has 483 successes and a sample size = 596.
What is the test statistic (z-score) for this sample? (Round to **3 decimal** places.)
test statistic = 

What is the p-value for this sample? (Round to **3 decimal** places.)
p-value = 

The p-value is...

less than (or equal to) 𝛼

greater than 𝛼

This test statistic leads to a decision to...

do not reject the null

accept the null

reject the null

As such, the final conclusion is that...

There is not sufficient sample evidence to support the alternate hypothesis claim that p1 is not equal to p2.

The sample data support the alternate hypothesis claim that p1 is not equal to p2.

To solve this problem, we will follow these steps:

1. Define the null and alternative hypotheses.
2. Calculate the pooled proportion.
3. Calculate the test statistic.
4. Find the p-value.
5. Make a decision based on the p-value and the significance level.

### Step 1: Define the Hypotheses

- \(H_0: p_1 = p_2\)
- \(H_a: p_1 \neq p_2\)

### Step 2: Calculate the Pooled Proportion

The pooled proportion \( \hat{p} \) is calculated as follows:

\[
\hat{p} = \frac{x_1 + x_2}{n_1 + n_2}
\]

where:
- \( x_1 \) = number of successes in the first sample = 539
- \( n_1 \) = total number in the first sample = 754
- \( x_2 \) = number of successes in the second sample = 483
- \( n_2 \) = total number in the second sample = 596

So,

\[
\hat{p} = \frac{539 + 483}{754 + 596} = \frac{1022}{1350} \approx 0.7563
\]

### Step 3: Calculate the Test Statistic

The test statistic for the difference in proportions is calculated using the formula:

\[
z = \frac{(\hat{p}_1 - \hat{p}_2)}{\sqrt{\hat{p} (1 - \hat{p}) \left( \frac{1}{n_1} + \frac{1}{n_2} \right)}}
\]

where:
- \( \hat{p}_1 = \frac{539}{754} \approx 0.7141 \)
- \( \hat{p}_2 = \frac{483}{596} \approx 0.8104 \)

So,

\[
z = \frac{0.7141 - 0.8104}{\sqrt{0.7563 \cdot (1 - 0.7563) \left( \frac{1}{754} + \frac{1}{596} \right)}}
\]

First, calculate the standard error (SE):

\[
SE = \sqrt{0.7563 \cdot 0.2437 \left( \frac{1}{754} + \frac{1}{596} \right)} \approx \sqrt{0.7563 \cdot 0.2437 \cdot 0.00251} \approx \sqrt{0.000462} \approx 0.0215
\]

Now, calculate \( z \):

\[
z = \frac{0.7141 - 0.8104}{0.0215} \approx \frac{-0.0963}{0.0215} \approx -4.481
\]

### Step 4: Find the P-Value

Using the standard normal distribution, we find the p-value for \( z = -4.481 \).

The p-value is twice the area to the right of \( |z| \):

\[
p\text{-value} = 2 \cdot P(Z > 4.481) \approx 2 \cdot 0.0000037 \approx 0.0000074
\]

### Step 5: Decision and Conclusion

The significance level \( \alpha \) is 0.05.

- Since \( p\text{-value} = 0.0000074 \) is less than 0.05, we reject the null hypothesis.

### Summary of Results

- **Test statistic**: \( z \approx -4.481 \)
- **P-value**: \( \approx 0.0000074 \)
- The p-value is **less than** the significance level (0.05).
- This test statistic leads to a decision to **reject the null**.
- **Conclusion**: The sample data support the alternate hypothesis claim that \( p_1 \) is not equal to \( p_2 \).

Do you want more details or have any questions?

Here are 8 relative questions that expand previous information:

1. How would the results change if the significance level were 0.01 instead of 0.05?
2. What are Type I and Type II errors in the context of this hypothesis test?
3. How can the power of this test be increased?
4. What assumptions must be met for this test to be valid?
5. How does sample size affect the results of a hypothesis test for proportions?
6. What is the interpretation of the p-value in this context?
7. How would you conduct a confidence interval for the difference in proportions?
8. What are alternative methods for comparing two population proportions?

**Tip**: Always check the assumptions of your statistical test to ensure valid results.

Learn how to conduct hypothesis testing for proportions with an example. Calculate the test statistic and p-value using sample data from two populations. Understand the decision-making process based on significance level and p-value.

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