Once a test statistic has been calculated, we calculate the P-value by using what we know about the distribution of the test statistic. For the test of proportions that meets the sample conditions (like in Question 1), we use the standard normal curve to calculate the P-value as an area under the standard normal curve. Since the P-value provides evidence used in support of the alternative hypothesis, the area we measure depends on the alternative hypothesis. Ask yourself: "If the null is in fact true, how likely are the data that you've gathered?"- Go to https://domathpathways.shinyapps.io/NormalDist/ @ and select "Find Probability." Suppose that a test statistic of > = -1.2 has been obtained. Then, The P-value equals: When the alternative is: The P-value is: (Rounded to four decimal places) How "unlikely" it is that we observed the sample data that Lower-tailed resulted in a test statistic of z=-1.2 or lower. (pnull value) The area on the right side of the test statistic under the standard normal curve. How "unlikely" it is that we observed the sample data that resulted in a test statistic of z=-1.2 or lower OR z=1.2 or Two-tailed higher. (p=ull value) The area on the right of the absolute value of the test statistic and the area on the left of the negative absolute value of the test statistic (i.e., more extreme). Hint Remember that a z-test statistic is from a standard normal distribution, which has a mean of 0 and a standard deviation of 1