A florist wants to determine if a new additive would help extend the life of cut flowers longer than the original additive. The florist randomly selects 20 carnations and randomly assigns 10 to the new additive and 10 to the original additive. After three weeks, 5 carnations placed in the new additive still looked healthy, and 3 carnations placed in the original additive still looked healthy. The difference in proportions (new – original) for the carnations that still looked healthy after three weeks was 0.2. Assuming there is no difference in the additives, 200 simulated differences in sample proportions are displayed in the dotplot. Using this dotplot and the difference in proportions from the samples, is there convincing evidence that the new additive was more effective?
Yes, because a difference in proportions of 0.2 or more occurred 41 out of 200 times, meaning the difference is statistically significant and the new additive is more effective.
Yes, because a difference in proportions of 0.2 or less occurred 159 out of 200 times, meaning the difference is statistically significant and the new additive is more effective.
No, because a difference in proportions of 0.2 or more occurred 41 out of 200 times, meaning the difference is not statistically significant and the new additive is not more effective.
No, because a difference in proportions of 0.2 or less occurred 159 out of 200 times, meaning the difference is not statistically significant and the new additive is not more effective.