That way you won’t get confused about which information pertains to which population. Before starting any of the inference methods, take a few moments to label each population. The most challenging part of using inference tools on problems dealing with two populations is keeping the information straight. In interviews with 350 individuals with at least some college, she determined that 112 of them consumed too much cholesterol per day. In interviews with 320 individuals who have at most an eighth-grade education, she determined that 114 of them consumed too much cholesterol. The term p 1 – p 2 in the numerator disappears because we are assuming that p 1 = p 2, so p 1 – p 2 = 0.Īll other steps for the hypothesis test remain the same as discussed in sub-competency 9.Ī nutritionist claims that the proportion of individuals who have at most an eighth-grade education and consume more than the USDA’s recommended daily allowance of 300 mg of cholesterol is higher than the proportion of individuals who have at least some college and consume too much cholesterol. The formula for the test statistic z 0 becomes: Substituting p̂ into the sample standard deviation expression gives: (Think about this a bit.) We define p̂ to be the pooled population proportion: Now if both Population 1 and Population 2 are the same in terms of the required proportion, they could be considered to be the “same” population. When calculating the test statistic z 0 (notice we use the standard normal distribution), we are assuming that the two population proportions are the same, p 1 = p 2 = p̂. Skipping most of the details, the null hypothesis is the assumed condition that the proportions from both populations are equal, H 0: p 1 = p 2, and the alternative hypothesis is one of the three conditions of non-equality.
0 kommentar(er)
