Answer:
Step-by-step explanation:
Given that:
The population proportion = 8.1% = 0.081
Sample size = 1000
The sample proportion [tex]\hat p[/tex] = 6.5% = 0.065
The null and the alternative hypothesis for these studies can be computed as:
[tex]\mathbf{H_o : p = 0.081} \\ \\ \mathbf{H_1 : p \neq 0.081}[/tex]
Since [tex]H_1[/tex] is not equal to the population proportion; then this is a two-tailed test.
The level of significance is given as 5% = 0.05
The standard error [tex]\sigma_p[/tex] of the sample proportion [tex]\hat p[/tex] can be computed as:
[tex]\sigma_p = \sqrt{\dfrac{p(1-p)}{n} }[/tex]
[tex]\sigma_p = \sqrt{\dfrac{0.081(1-0.081)}{1000} }[/tex]
[tex]\sigma_p = \sqrt{\dfrac{0.081(0.919)}{1000} }[/tex]
[tex]\sigma_p = 0.0086[/tex]
The z score test statistic is calculated as:
[tex]z =\dfrac{\hat p - p}{\sigma_p}[/tex]
[tex]z =\dfrac{0.065- 0.081}{0.0086}[/tex]
z = −1.860
For a two-tailed test, the p-value = [tex]2 \times P(Z < -|z|)[/tex]
[tex]p-value = 2 \times P(Z < - 1.86)[/tex]
p-value = 2 × 0.0314
p-value = 0.0628
Rejection Criteria: To reject [tex]H_o[/tex] ; if the p-value is less than the level of significance ∝
The decision rule: We failed to reject the null hypothesis since the p-value is greater than the level of significance ∝.
Thus, there is insufficient evidence to conclude that p is not equal to 0.081