Respuesta :
Using the z-distribution, since the p-value of the test is of 0.057 > 0.05, there is not enough evidence that the population proportion of successes does not equal 0.50.
What are the hypotheses tested?
At the null hypotheses, we test if the proportion of successes equals 0.5, hence:
[tex]H_0: p = 0.5[/tex]
At the alternative hypotheses, we test if it does not equal, hence:
[tex]H_1: p \neq 0.5[/tex]
What is the test statistic?
The test statistic is given by:
[tex]z = \frac{\overline{p} - p}{\sqrt{\frac{p(1-p)}{n}}}[/tex]
In which:
- [tex]\overline{p}[/tex] is the sample proportion.
- p is the proportion tested at the null hypothesis.
- n is the sample size.
For this problem, the parameters are given by:
[tex]n = 40, \overline{p} = \frac{14}{40} = 0.35, p = 0.5[/tex]
Hence the test statistic is given by:
[tex]z = \frac{\overline{p} - p}{\sqrt{\frac{p(1-p)}{n}}}[/tex]
z = (0.35 - 0.5)/(0.5/sqrt(40))
z = -1.9.
What is the decision?
Using a z-distribution calculator, considering a two-tailed test, as we are testing if the proportion is different of a value, with z = -1.9, we get that the p-value of the test is of 0.057.
Since the p-value of the test is of 0.057 > 0.05, there is not enough evidence that the population proportion of successes does not equal 0.50.
More can be learned about the z-distribution at https://brainly.com/question/16313918
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