From the test the parson wants, and the sample data, we build the test hypothesis and find the p-value.
Suppose someone wants to claim that more than 55% of adult Catholics in the United States are in favor of allowing women to become priests.
At the null hypothesis, it is tested that the proportion is of at most 55%, that is:
At the alternative hypothesis, it is tested that the proportion is of more than 55%, that is:
Since we are testing only one proportion, it is a one-sample test. Since we are testing only if the proportion is higher/lower, in this case higher, than a value, it is a one-tailed test.
P-value:
To find the p-value of the test, we first have to find the test statistic.
The test statistic is:
In which X is the sample mean, is the value tested at the nu
0.55 is tested at the null hypothesis:
This means that
From the sample:
Survey of 507, 59% answer yes, thus:
Value of the test statistic:
P-value from the test statistic:
The p-value of the test is the probability of finding a sample proportion above 1.81, which is 1 subtracted by the p-value of z = 1.81.
Looking at the z-table, z = 1.81 has a p-value of 0.9649.
1 - 0.9649 = 0.0351.
Thus, the p-value of the test is of 0.0351.
For another example of a similar problem, you can check brainly.com/question/24166849