NSU Job justifications Paper.

I have been chosen to create an outpatient immunization clinic. I have chosen to include the following job positions: Director of Outpatient Immunization, Business Manager, Clinical Manager, Nurses and Receptionists to be part of my organization. Please provide justification as to why these positions are vital to an outpatient immunization clinic. Make sure all sources are from 2017-present and are in APA format

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1 Director of the Outpatient Immunization Clinic Clinical Manager Business Manager Nurses Receptionist 2 3 Health and Medication Questions 1. What is the graphical relationship of the signs in the alternative hypothesis to the area of rejection of the null hypothesis? In a graphical representation, a test is one-tailed when the sample mean is either larger or smaller than the population of the study, while two-tailed is when the mean sample is smaller or larger than the population. For example, a two-tailed test would be used if you have two groups, A and B, and your interest in seeing if group B scored higher or lower in terms of marks. When dealing with a one-tailed test, the statistical significance level is not divided into two directions (Emmert-Streib et al., 2019). So, if the sample tested falls in the upper tail (one-sided critical area), we accept the alternative hypothesis and reject the null hypothesis. The two-tailed test is more conservative because of its involvement in dividing the statistical level into two. Any statistics point overhead or under the upper or lower parameters is observed outside of the recognition range and falls into the rejection assortment. In this case, we usually accept the alternative hypothesis. 2. What are Type I and Type II errors? Why should we care? Type I error is a wrong conclusion to a test. It is also referred to as a false-positive error. It’s significant to note that the Type I inaccuracy does not imply that we incorrectly accept an experiment’s alternative hypothesis. On the other hand, Type II error is not rejecting a false hypothesis because of a lack of statistical power to detect evidence (Kalnins, 2018).). The latter can be controlled by increasing the sample size; the larger the statistical test’s power, the less likely it is to make a type II error. It is the opposite of type 1 error, where a statistician fails to reject a null hypothesis that is false in real-time. Type I and Type II inaccuracies are convincingly important since they help us support our hypothesis. Neither of them should be 4 ignored. Inflating Type, I error will likely leave the researcher with little to no evidence to support the hypothesis. They need to be controlled so that the errors are not at the expense of the other. 5 References Emmert-Streib, F, & Dehmee,M. (2019). Understanding statistical hypothesis testing. The logic of statistical inference. Machine learning and knowledge extractions,1(3),945961. Kalnins,A(2018). Multicollinearity. How common factors cause type 1