a. Test the claim asing a hypothesis test.
What are the null and alternative hypotheses for the hypothesis test?
A. $H_{0}: p_{1} \leq p_{2}$
$H_{1}: p_{1} \neq p_{2}$
D. $H_{0}: p_{1} \neq p_{2}$
$H_{1}: p_{1}=p_{2}$
B. $H_{0}: p_{1}=p_{2}$
$H_{1}: p_{1}> p_{2}$
E. $H_{0}: p_{1} \geq p_{2}$
$H_{1}: p_{1} \neq p_{2}$
C.
\[
\begin{array}{l}
H_{0}: p_{1}=p_{2} \\
H_{1}: p_{1} \neq p_{2}
\end{array}
\]
F.
\[
\begin{array}{l}
H_{0}: p_{1}=p_{2} \\
H_{1}: p_{1}< p_{2}
\end{array}
\]
Identify the test statistic.
\[
z=-0.35
\]
(Round to two decimal places as needed.)
Identify the P-value.
P-value $=$
(Round to three decimal places as needed.)

Step 1 ：First, we need to identify the null and alternative hypotheses for the hypothesis test. The null hypothesis, denoted by $H_{0}$, is a statement of no effect or no difference. The alternative hypothesis, denoted by $H_{1}$ or $H_{a}$, is a statement that contradicts the null hypothesis. In this case, the null and alternative hypotheses are $H_{0}: p_{1}=p_{2}$ and $H_{1}: p_{1} eq p_{2}$ respectively.

Step 2 ：Next, we identify the test statistic. The test statistic is a measure of how far our sample statistic is from the hypothesized population parameter, in terms of standard errors. In this case, the test statistic is $z=-0.35$.

Step 3 ：Then, we need to identify the P-value. The P-value is the probability of observing a test statistic as extreme as the one calculated, assuming the null hypothesis is true. In this case, we don't have the P-value given, so we can't identify it.

Step 4 ：Finally, we would compare the P-value to our significance level to decide whether to reject the null hypothesis. If the P-value is less than the significance level, we reject the null hypothesis. If the P-value is greater than the significance level, we fail to reject the null hypothesis. However, since we don't have the P-value, we can't make this decision.