
Using the z-distribution, it is found that there is enough evidence to conclude that taking a low-dose aspirin each day reduces the chance of developing cancer.
At the null hypotheses, it is tested if there is no difference among the proportions, that is:
[tex]H_0: p_1 - p_2 = 0[/tex]
At the alternative hypotheses, it is tested if the aspirir proportion is lower, that is:
[tex]H_0: p_1 - p_2 < 0[/tex]
For each sample, they are given by:
[tex]p_1 = \frac{15}{500} = 0.03, s_1 = \sqrt{\frac{0.03(0.97)}{500}} = 0.0076[/tex]
[tex]p_2 = \frac{26}{500} = 0.052, s_2 = \sqrt{\frac{0.052(0.948)}{500}} = 0.0099[/tex]
Hence, for the distribution of differences, they are given by:
[tex]\overline{p} = p_1 - p_2 = 0.03 - 0.052 = -0.022[/tex]
[tex]s = \sqrt{s_1^2 + s_2^2} = \sqrt{0.0076^2 + 0.0099^2} = 0.0125[/tex]
It is given by:
[tex]z = \frac{\overline{p} - p}{s}[/tex]
In which p = 0 is the value tested at the null hypothesis.
Hence:
[tex]z = \frac{\overline{p} - p}{s}[/tex]
[tex]z = \frac{-0.022 - 0}{0.0125}[/tex]
z = -1.76.
Considering a left-tailed test, as we are testing if the proportion is less than a value, the critical value is [tex]z^{\ast} = -1.645[/tex]
Since the test statistic is less than the critical value for the left-tailed test, there is enough evidence to conclude that taking a low-dose aspirin each day reduces the chance of developing cancer.
More can be learned about the z-distribution at https://brainly.com/question/26454209