Suppose the Acme Drug Company develops a new drug, designed to prevent colds. The company
states that the drug is equally effective for men and women. To test this claim, they choose a
simple random sample of 110 women and 190 men from a population of 100,000 volunteers.
At the end of the study, 28% of the women caught a cold; and 61% of the men caught a cold.
Based on these findings, can we reject the company's claim that the drug is equally effective for
men and women? Use a 0.05 level of significance.
The value of the pooled proportion is computed as
"=\\dfrac{0.28(110)+0.61(190)}{110+190}=0.489"
The following null and alternative hypotheses need to be tested:
"H_0:p_1=p_2"
"H_1:p_1\\not=p_1"
This corresponds to a two-tailed test, and a z-test for two population proportions will be used.
Based on the information provided, the significance level is "\\alpha = 0.05," and the critical value for a two-tailed test is "z_c = 1.96."
The rejection region for this two-tailed test is "R = \\{z:|z|>1.96\\}."
The z-statistic is computed as follows:
"=\\dfrac{0.28-0.61}{\\sqrt{0.489(1-0.489)(1\/110+1\/190)}}=-5.5101"
Since it is observed that "|z|=5.5101>1.96=z_c," it is then concluded that the null hypothesis is rejected.
Using the P-value approach:
The p-value is "p=2P(z<-5.5101)=0," and since "p=0<0.05=\\alpha," it is concluded that the null hypothesis is rejected.
Therefore, there is enough evidence to claim that the population proportion "p_1" is different than "p_2," at the "\\alpha = 0.05" significance level.
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