Two researchers conduct separate studies to test Ho: p=0.50 against Ha: p0.50, each with n = 400. Researcher A gets 226 observations in the category of interest, and p=226/400=0.565. Researcher B gets 225 in the category of interest, and p = 225/400=0.5625. Complete parts (a) through (d) below. A ... c. From (a) and (b), explain why important information is lost by reporting the result of a test as "P-value ≤ 0.05" versus "P-value > 0.05," or as "reject Ho" versus "Do not reject Ho." instead of reporting the actual P-value. OA. The actual P-value is more informative because the probability of a Type I or Type II error depends on the size of the significance level, a. OB. The actual P-value is more informative because a proportion of tests equal to the significance level, a, is expected to reject the null hypothesis by chance even if it is true. C. The actual P-value is more informative because the strength of the evidence varies based on the value.

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Two researchers conduct separate studies to test Ho: p=0.50
against Ha: p=0.50, each with n = 400. Researcher A gets 226
observations in the category of interest, and
p=226/400 =0.565. Researcher B gets 225 in the category of
interest, and p = 225/400=0.5625. Complete parts (a) through
(d) below.
c. From (a) and (b), explain why important information is lost by
reporting the result of a test as "P-value ≤0.05" versus
"P-value > 0.05," or as "reject Ho" versus "Do not reject Ho
instead of reporting the actual P-value.
OA. The actual P-value is more informative because the
probability of a Type I or Type Il error depends on the
size of the significance level, a.
B. The actual P-value is more informative because a
proportion of tests equal to the significance level, a, is
expected to reject the null hypothesis by chance even if
it is true.
OC. The actual P-value is more informative because the
strength of the evidence varies based on the value.
d. Find the 99% confidence interval for p for both researchers.
Explain how this method shows that, in practical terms, the two
studies had very similar results.
8.8
The confidence interval for B is
(Round to three decimal places as needed.)
Explain why the two studies had very similar results.
The confidence interval for A is (
OA. The studies had similar results because both showed
that it is not plausible that the population proportion for
the category of interest is 0.50.
B. The studies had very similar results because the
P-values were very close to the significance level in
each study.
OC. The studies had similar results because both showed
that 99% of the time. the results will fall within verv
Transcribed Image Text:Two researchers conduct separate studies to test Ho: p=0.50 against Ha: p=0.50, each with n = 400. Researcher A gets 226 observations in the category of interest, and p=226/400 =0.565. Researcher B gets 225 in the category of interest, and p = 225/400=0.5625. Complete parts (a) through (d) below. c. From (a) and (b), explain why important information is lost by reporting the result of a test as "P-value ≤0.05" versus "P-value > 0.05," or as "reject Ho" versus "Do not reject Ho instead of reporting the actual P-value. OA. The actual P-value is more informative because the probability of a Type I or Type Il error depends on the size of the significance level, a. B. The actual P-value is more informative because a proportion of tests equal to the significance level, a, is expected to reject the null hypothesis by chance even if it is true. OC. The actual P-value is more informative because the strength of the evidence varies based on the value. d. Find the 99% confidence interval for p for both researchers. Explain how this method shows that, in practical terms, the two studies had very similar results. 8.8 The confidence interval for B is (Round to three decimal places as needed.) Explain why the two studies had very similar results. The confidence interval for A is ( OA. The studies had similar results because both showed that it is not plausible that the population proportion for the category of interest is 0.50. B. The studies had very similar results because the P-values were very close to the significance level in each study. OC. The studies had similar results because both showed that 99% of the time. the results will fall within verv
Two researchers conduct separate studies to test Ho: p=0.50
against Ha: p0.50, each with n = 400. Researcher A gets 226
observations in the category of interest, and
p=
6=226/400=0.565. Researcher B gets 225 in the category of
interest, and p = 225/400=0.5625. Complete parts (a) through
(d) below.
a. Find the z-scores and P-values for both researchers.
Researcher A
Z =
Researcher B
Z =
P-value =
P-value =
(Round the z-scores to two decimal places as needed. Round
the P-values to three decimal places as needed.)
The result in case A is
The result in case B is
b. Using α = 0.01, indicate in each case whether the result is
"statistically significant." Interpret.
What does the statistical significance of the results above imply?
OA. A difference in conclusions between two hypothesis
tests does not necessarily mean that the test with the
result that is deemed "statistically significant" is actually
much more significant than the test with the result that
is deemed "not statistically significant."
OB. Hypothesis tests that produce similar P-values always
have the same conclusion.
OC. A difference in conclusions between two hypothesis
tests always means that the test with the result that is
deemed "statistically significant" is much more
significant than the test with the result that is deemed
"not statistically significant."
O D. Hypothesis tests that result in the same conclusion do
not necessarily have the same P-value.
Transcribed Image Text:Two researchers conduct separate studies to test Ho: p=0.50 against Ha: p0.50, each with n = 400. Researcher A gets 226 observations in the category of interest, and p= 6=226/400=0.565. Researcher B gets 225 in the category of interest, and p = 225/400=0.5625. Complete parts (a) through (d) below. a. Find the z-scores and P-values for both researchers. Researcher A Z = Researcher B Z = P-value = P-value = (Round the z-scores to two decimal places as needed. Round the P-values to three decimal places as needed.) The result in case A is The result in case B is b. Using α = 0.01, indicate in each case whether the result is "statistically significant." Interpret. What does the statistical significance of the results above imply? OA. A difference in conclusions between two hypothesis tests does not necessarily mean that the test with the result that is deemed "statistically significant" is actually much more significant than the test with the result that is deemed "not statistically significant." OB. Hypothesis tests that produce similar P-values always have the same conclusion. OC. A difference in conclusions between two hypothesis tests always means that the test with the result that is deemed "statistically significant" is much more significant than the test with the result that is deemed "not statistically significant." O D. Hypothesis tests that result in the same conclusion do not necessarily have the same P-value.
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