Week 7 Discussion: Hypothesis Testing
Chamberlain University Week 7 Discussion: Hypothesis Testing-Step-By-Step Guide
This guide will demonstrate how to complete the Chamberlain University Week 7 Discussion: Hypothesis Testing assignment based on general principles of academic writing. Here, we will show you the A, B, Cs of completing an academic paper, irrespective of the instructions. After guiding you through what to do, the guide will leave one or two sample essays at the end to highlight the various sections discussed below.
How to Research and Prepare for Week 7 Discussion: Hypothesis Testing
Whether one passes or fails an academic assignment such as the Chamberlain University Week 7 Discussion: Hypothesis Testing depends on the preparation done beforehand. The first thing to do once you receive an assignment is to quickly skim through the requirements. Once that is done, start going through the instructions one by one to clearly understand what the instructor wants. The most important thing here is to understand the required format—whether it is APA, MLA, Chicago, etc.
After understanding the requirements of the paper, the next phase is to gather relevant materials. The first place to start the research process is the weekly resources. Go through the resources provided in the instructions to determine which ones fit the assignment. After reviewing the provided resources, use the university library to search for additional resources. After gathering sufficient and necessary resources, you are now ready to start drafting your paper.
How to Write the Introduction for Week 7 Discussion: Hypothesis Testing
The introduction for the Chamberlain University Week 7 Discussion: Hypothesis Testing is where you tell the instructor what your paper will encompass. In three to four statements, highlight the important points that will form the basis of your paper. Here, you can include statistics to show the importance of the topic you will be discussing. At the end of the introduction, write a clear purpose statement outlining what exactly will be contained in the paper. This statement will start with “The purpose of this paper…” and then proceed to outline the various sections of the instructions.
How to Write the Body for Week 7 Discussion: Hypothesis Testing
After the introduction, move into the main part of the Week 7 Discussion: Hypothesis Testing assignment, which is the body. Given that the paper you will be writing is not experimental, the way you organize the headings and subheadings of your paper is critically important. In some cases, you might have to use more subheadings to properly organize the assignment. The organization will depend on the rubric provided. Carefully examine the rubric, as it will contain all the detailed requirements of the assignment. Sometimes, the rubric will have information that the normal instructions lack.
Another important factor to consider at this point is how to do citations. In-text citations are fundamental as they support the arguments and points you make in the paper. At this point, the resources gathered at the beginning will come in handy. Integrating the ideas of the authors with your own will ensure that you produce a comprehensive paper. Also, follow the given citation format. In most cases, APA 7 is the preferred format for nursing assignments.
How to Write the Conclusion for Week 7 Discussion: Hypothesis Testing
After completing the main sections, write the conclusion of your paper. The conclusion is a summary of the main points you made in your paper. However, you need to rewrite the points and not simply copy and paste them. By restating the points from each subheading, you will provide a nuanced overview of the assignment to the reader.
How to Format the References List for Week 7 Discussion: Hypothesis Testing
The very last part of your paper involves listing the sources used in your paper. These sources should be listed in alphabetical order and double-spaced. Additionally, use a hanging indent for each source that appears in this list. Lastly, only the sources cited within the body of the paper should appear here.
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Week 7 Discussion: Hypothesis Testing
Hypothesis testing is a statistical approach whereby the researcher or data analysist tests the assumption concerning the population parameter. In the hypothesis testing process, different data analysts may use disparate approaches. The methodology applied by data analysts may depend on the dataset used. In other words, the methodology used usually depends on the data that has been collected as well as the reason for data analysis. In most cases, hypothesis testing is applied in the evaluation of the plausibility or credibility of the hypothesis by the use of sample data. There is null and alternative hypothesis (Kunisky, Wein, & Bandeira, 2019). Null hypothesis is stated in the negative statement while alternative hypothesis is stated in a positive statement. In other words, null hypothesis is a hypothesis of equality between the population parameters. For instance, a null hypothesis may state that the population mean value is zero. Since the alternative hypothesis is the opposite of the null hypothesis, it can be stated that the population mean return is equal to zero (List, Shaikh, & Xu, 2019). The two sides are mutually exclusive and at a given instance, only one can be true. In other words, one of the two hypotheses will be true at the end of the study.
A hypothesis test study that would help my work in some way is: The effect of body mass index on blood pressure. In this hypothesis test study, there will be the determination of whether there is significant effects of body mass index on the blood pressure. The two variables that will be tested are “Body Mass Index” and “Blood Pressure” In other words, the dependent variable is Blood Pressure while the independent variable is Body Mass Index. Alternatively, the variable that will be tested is Body Mass Index. From the two variables, the hypothesis can be stated as follows:
Null Hypothesis (Ho): There is no significant effect of body mass index on the blood
Pressure
Alternative Hypothesis (H1): There is no significant effect of body mass index on the
blood pressure
The outcome of the study is expected to show that there is significant impacts of body mass index on high blood pressure. In other words, it is expected that there is significant effective of the independent variable on the dependent variable. Before undertaking the hypothesis test, there is need to collect and arrange data to ensure effective outcomes. A student t-test can be applied in the hypothesis testing to ensure effective outcomes. In the above cases, when the null hypothesis is rejected, the alternative hypothesis is used to make a conclusion and when the null hypothesis is accepted, it is used to make meaningful conclusion.
If the null hypothesis is rejected in the above case, then the expected outcome will stand. In other words, there will be a conclusion that there is a significant effect of body mass index on the blood pressure. On the other hand, accepting null hypothesis means that the expected outcome will not stand. Therefore, rejecting the null hypothesis will not change my conclusion or actions in some ways while accepting the null hypothesis may change my conclusion or action in different ways.
References
Kunisky, D., Wein, A. S., & Bandeira, A. S. (2019). Notes on computational hardness of hypothesis testing: Predictions using the low-degree likelihood ratio. arXiv preprint arXiv:1907.11636. https://arxiv.org/abs/1907.11636
List, J. A., Shaikh, A. M., & Xu, Y. (2019). Multiple hypothesis testing in experimental economics. Experimental Economics, 22(4), 773-793. https://doi/10.1007/s10683-018-09597-5
I would like to study the hospital readmission rate of patients who have early outpatient follow up appointments versus those who do not. As healthcare professionals we know that readmissions are costly to both the hospital and the patient. The two disease processes with the highest readmission rates among them are COPD and heart failure (Song and Walter, 2017). I would like to hypothesis that those who follow up within a month of hospital discharge would have a decrease in readmission rate within 3 months to the hospital when compared to those who do not follow up.
The readmission rate is my variable in the scenario. The hypothesis would than read patients with COPD or heart failure who follow up within a month of discharge will not have a readmission to the hospital within 3 months. Ho where p= 0 for zero readmission. If this is untrue Ha p ≠ 0 because there was at least one readmission. This would be considered the alternative hypothesis (Chamberlain University, 2021). I hypothesize that the first (Ho = 0) will be true as this knowledge is not necessarily new however, many patients still do not follow up quickly after discharge. Patient education is a priority when discussing follow ups after discharge.
Brennaa Sullivan
References:
Chamberlain University. (2021). Math225. Week 6 Lesson: From Samples to Population [Online Lesson]. Downers Grove, IL: Adtalem.
Song, J., Walter, M. (2017). Effect of Early Follow-Up After Hospital Discharge on Outcomes in Patients with Heart Failure or Chronic Obstructive Pulmonary Disease: A Systematic Review. Health Quality Ontario. https://pubmed.ncbi.nlm.nih.gov/28638496/Links to an external site.
Confidence interval refers to the interval estimate around the mean or average. In other words, confidence interval may refer to the set of values around or close to the mean either in the negative or positive ways. In most cases, statistical studies do not have 100% confidence or certainty that the outcome of data analysis will be true. In the research process, there is always the likelihood that a hypothesis can be accepted (failed to be rejected) or rejected; this is always attributed to the confidence interval (Ambrose, 2018). Confidence intervals are important in data analysis because they aid in the determination of the accuracy of the mean. A confidence with smaller range indicates that the estimates are more accurate. On the other hand, when there is huge range or larger figure, the estimate may be considered inaccurate. To better understand the concept of confidence interval, the illustration below is a basic and more accurate definition. A confidence interval of 95% indicates that 95% of the studies will incorporate the true mean; on the other hand, 5% of the studies will not. In other words, there are five out of 100 that the research is wrong.
Confidence interval may also refer to the range of values from given sets of sample data that are most likely to include the true mean of the population. Confidence intervals often form or are used to determine the accuracy in the data analysis processes. With the confidence interval, an individual can be sure that they have captured the mean of a given population. When the confidence level is so small, there is a high possibility of obtaining accurate outcomes in the data analysis processes. On the other hand, there might be a problem in the long run. For instance, if one says that they are certain of scoring 99%, the range of the data being calculated may be so big. For example, an individual may be 99% certain of scoring 10 to 100 on the examination ( Peterson & Kegler, 2020).
In the healthcare undertakings, there are different measurements that are always recorded. These measurements are often recorded with much accuracy using the mean and confidence level. For example, blood glucose is something that is often measured in the healthcare system for the critically ill patients. There are various approaches of controlling blood glucose levels. Confidence interval can be applies to formulate correct approaches of delivering the best glucose control mechanisms. In most cases, hypothesis testing and confidence interval are applied together in the healthcare processes to determine the correlation that exists. Confidence intervals are essential approaches in statistical analysis.
Confidence Interval uses data from a sample to estimate a population parameter and hypothesis testing using data from a sample to test a specified hypothesis. Both hypothesis testing and analysis of confidence interval can aid in answering the research questions, the objectives of the research and the hypothesis formulated based on the research questions. Hypothesis testing and CI are used together in health care research to determine the correlation of variables to establish a probability value for improving patient outcomes in certain populations in the clinical setting.
References
Ambrose, J. (2018). Clinical inquiry and hypothesis testing. Grand Canyon University. Retrieved from https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3Links to an external site.
Peterson, A. B., & Kegler, S. R. (2020). Deaths from Fall-Related Traumatic Brain Injury – United States, 2008-2017. MMWR: Morbidity & Mortality Weekly Report, 69(9), 225–230. https://doi-org.lopes.idm.oclc.org/10.15585/mmwr.mm6909a2