For the Importance of Outcome Measurements Activity, write a paper including the following:Explanation of the Donabedian model of performance improvement in health care and how the model impacts the work of the quality improvement professionalExplanation of the difference between structure, process, and outcome measures and examples of these types of measures from your experience, including how you use these variables to improve your workplaceExamples of clinical outcome measures, why these measures are important for improving clinical care and the important features of effective outcome measuresYour response should be 3-5 pages using 12-point times new roman font, double-space, with 1-inch margins submitted as a word document above.

Consider a situation that you might want to study through a statistical lens. The situation would require you to study a small sample and make an inference to the population. For example, you might want to understand how likely children are to complete their homework when considering their individual characteristics or maybe you want to understand if children who eat broccoli are more likely to complete their homework than those who do not.Respond to the following in a minimum of 100 words without any reference:How would you select a test that was appropriate to answer your question?How would you rule out tests that were not appropriate to answer your question? Post 2 replies to classmates or your faculty member. Be constructive and professional.This is one of the responses from another student that I need to reply to:"To determine the kind of test to use will depend on the population sample collected. For instance, we can use the T-Test to find the difference between the two pairs of the mean. In our case, we need to determine the individual characteristics which can be easily done using the ANOVA test. First, we need to find quality data sample population and compare those population means with three or more population mean. The main idea is to figure out how much of the total variance is from either the variance between the groups or the variance within the groups. Therefore, our Null hypothesis will be all the population mean of those groups is equal while the alternative hypothesis will be at least one mean of those groups is different. As illustrated below.H0: µ1=µ2=µ3Ha: At least one the mean differs from the others.For this case, we have to test how these means differ from other mean populations. We will use the concept of variance to test if our null hypothesis means is likely equal to other means for us to accept or reject if one of the means is not likely to be equal to other means. For better results, we need to study our population sampling as we use the concept of variance. Once we find the one or more mean differ, we have to validate the result because the quality of data affects the outcome, and also it will depend on our population sample."