HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Grand Canyon University HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research-Step-By-Step Guide

This guide will demonstrate how to complete the Grand Canyon University HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research 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 HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research                       

Whether one passes or fails an academic assignment such as the Grand Canyon University HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research 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 HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research                       

The introduction for the Grand Canyon University HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research 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 HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research                       

After the introduction, move into the main part of the HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research  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 HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research                       

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 HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research                       

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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Sample Answer for HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research Included After Question

Topic 3 DQ 2

Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research. Provide a workplace example that illustrates your ideas.

A Sample Answer For the Assignment: HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Title:  HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

To understand how hypothesis testing and confidence intervals (CI) work together we must first understand what exactly they are. Hypothesis Tests are tests conducted by forming two opposing hypothesis (Research HA and Null Ho) and attempting to validate each in order to reach a possible outcome. Confidence Intervals are a “range of likely values of the parameter with a specified level of confidence (similar to a probability)” (Sullivan, 2022).

Both of these are known as inferential methods which both rely on approximated sampling distributions. CI is used to find a range of possible values and an estimate on the overall accuracy of the parameter value. Hypothesis testing is useful because it tells us how confident we can be when drawing conclusions about the parameter of our sample population.HLT-362 Topic 3 DQ 2

An example of this is testing the overall performance of a new medication being offered at a clinic. One must hypothesise the effect it will have on the patient population and try to find the parameters on the satisfaction of those taking said medication. By using these two methods in conjunction, the provider can have a good educated guess on the outcome and prepare accordingly.

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References

• Sullivan, L. (2022, January 1). Confidence Intervals. Retrieved from Boston University School of Public Health: https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_confidence_intervals/bs704_confidence_intervals_print.html

A Sample Answer 2 For the Assignment: HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Title:  HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

The hypothesis is a question the researcher would like to answer. A hypothesis drives a better outcome for patient care that goes evidence-based practice. The person must collect data in a controlled manner designated best to test the hypothesis. When using the Null hypothesis as current information, the alternative hypothesis attempts to reject the null. At the same time, the Ho and the Ha are mathematic opposites. Clinical significance is the application in improving the quality of life of an individual and provides the bridge from health research to patient care (Ambrose, 2018).HLT-362 Topic 3 DQ 2

While confidence intervals and hypothesis tests are similar, they contain inferential methods relying upon sampling. The LOC is a percentage of confidence level in deciding the difficulty of rejecting the hypothesis. Most people doing this research are > 90% LOC; otherwise, the test would not be warranted. The level of significance is α=1-c. Both the LOC and level of relevance reflect how sure you are of whether the data is making the correct decision or not.

The American Heart Association guidelines for resuscitation were based on the pneumonic of ABC- Airway, Breathing, and Circulation. The pneumonic is the null hypothesis. The alternative view was the use of Circulation, airways, and breathing. The research data reflected the Ha > Ho. The concentration of effective quality chest compressions leads to a worldwide change in how CPR is performed. The LOC was high enough to recruit large city Fire Dept such as Phoenix Fire to provide data regarding cardiac arrest and outcomes.

References HLT-362 Topic 3 DQ 2

• Ambrose, J. (2018a). Applied Statistics for Health Care. Grand Canyon University. https://doi.org/https://lc.gcumedia.com/hlt362v/applied-statistics-for-health- care/v1.1/#/chapter/3

A Sample Answer 3 For the Assignment: HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Title:  HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Both hypothesis testing and confidence interval are necessary for determining the validity of the research. Ambrose describes both type I and type II errors as flaws in the research outcomes that can be avoided with proper data analysis (2018). The text even further states “The researcher has an ethical responsibility to avoid making a type I or II error” (Ambrose, 2018). It falls upon nursing leadership to review current research and implement evidence-based nursing care and interventions. Accepting and promoting false research can ultimately create negative outcomes for patients and the care they receive. 

Resource: HLT-362 Topic 3 DQ 2

• Ambrose, J. (2018). Clinical inquiry and hypothesis testing. Applied Statistics for Health Care. https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3

A Sample Answer 4 For the Assignment: HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Title:  HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Great discussion this week, sharing your examples of how hypothesis testing and confidence intervals are used together in health care practice. You also highlighted the criteria we use in rejecting the null hypothesis. In summary, this week we were able to:

• Evaluate hypothesis testing approaches and their applications to health care.
• Describe the roles of dependent and independent variables in hypothesis testing.
• Describe the evidence used to “reject” or “do not reject” the null hypothesis.
• Evaluate the relationship between hypothesis testing and confidence intervals.

Watch “When Should I Use Qualitative vs. Quantitative Research?” on the YouTube website located at http://www.youtube.com/watch?v=638W_s5tRq8&feature=youtube_gdata to help you understand when you use qualitative and quantitative research in your clinical practice.

This short video was very helpful. Thank you for sharing it. HLT-362 Topic 3 DQ 2

My understanding of the difference between the use of Qualitative vs. Quantitative research is: Quantitative studies rely on numerical or measurable data. In contrast, qualitative studies rely on personal accounts or documents that illustrate in detail how people think or respond within society. For example, qualitative data is collected by surveys and questionnaires,  interviews, focus groups, and observations just to mention some. Quantitative has a numeral value therefore it is measured in numerical value. 

Hypothesis testing and confidence intervals are used together in healthcare research.  A hypothesis is a forecast statement of what will occur between two variables.  The independent and dependent variables are identified in the hypothesis and analyzed with gathered data to show correlations and relationships between variables.  A hypothesis is created when variables are identified.  

A confidence interval (CI) is an interval estimate of the mean which is a range of values of the data.  These values are close to the mean in a negative or positive direction.  The CI shows the risks of being wrong. If the CI reduces the risk of error increases.  A CI of 95% says that 5% of the mean will not be true yet 95% will be a true mean.  (Ambrose, 2018)HLT-362 Topic 3 DQ 2

A workplace example where the CI is used is a study suggesting that working shift work for long hours during pregnancy can be associated with adverse pregnancy risks.  The study showed that working a fixed night shift measured a CI of 95% with increased odds of miscarrying when compared to standard working hours. 

The study also revealed that working rotating shifts revealed a CI of 95% of increasing odds for preterm delivery. (Cai et al., 2019)  Using CIs with multiple variables, this study concluded that pregnant women increase risks of adverse pregnancy outcomes if working rotating shifts, fixed night shifts or longer hours.HLT-362 Topic 3 DQ 2

I work in a female-dominated industry and department.  I frequently see my pregnant colleagues being placed on alternative duty or light duty while pregnant.  This study concerns me because the “light duty” does not decrease their hours but instead keeps them from working “on their feet” all shift.  It would be interesting to see if these coworkers have increased adverse pregnancies in their work situations.

References

• Ambrose, J. (2018). Applied Statistics for Health Care. Gcumedia.com. https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3
• Cai, C., Vandermeer, B., Khurana, R., Nerenberg, K., Featherstone, R., Sebastianski, M., & Davenport, M. H. (2019). The impact of occupational shift work and working hours during pregnancy on health outcomes: a systematic review and meta-analysis. American Journal of Obstetrics and Gynecology, 221(6). https://doi.org/10.1016/j.ajog.2019.06.051

A Sample Answer 5 For the Assignment: HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Title:  HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Both confidence intervals and hypothesis tests are inferential methods that depend on a sample distribution that is approximated. Confidence intervals are used to measure a population parameter using data from a survey. Hypothesis experiments are used to test a hypothesis using data from a study. Hypothesis testing necessitates the presence of a parameter that has been hypothesized. A hypothesis test determines whether the outcome is exceptional, whether it is reasonable chance variation, or whether it is too extreme to be considered chance variation.

In health-care science, hypothesis testing, and confidence intervals are used together. This is used as an interval estimation for the mean with confidence interval (CI). A confidence interval (CI) is a set of values that are like the mean and can affect the direction in either a positive or negative way. Means using a procedure that contains the population mean with a defined proportion of the time, usually 95 percent or 99 percent of the time, are given a confidence interval (CI).

The CI is the range in which the researcher could be incorrect. A 95% confidence interval indicates that 95% of a research sample will contain the true mean, while the remaining 5% will not. Confidence intervals will help you compare the accuracy of various estimates with this in mind. For example, 95 percent of the data collected in a test survey of 100 participants will be correct, while five out of 100 will be incorrect.

If the 95 percent is decreased, the chance of error increases (Ambrose, 2018). Since hypothesis testing and confidence intervals are used together in health care research, this is important to note.

If you wanted to know the mean of temperatures obtained in a hospital with COVID-19 patients, you’d need to think about hypothesis testing and confidence intervals. Since it’s necessary to have a true mean of the temperatures of the sample collected, a CI of 95 percent will be better than a CI of 90 percent for this example. This is because the CI is determined by first determining the sample size, then determining the mean and standard deviation, and finally determining the degree of confidence interval.

It’s crucial to understand analytical quantitative analysis, which requires hypothesis testing and confidence intervals, to produce reliable findings from samples for the populations being studied. This is particularly relevant in health care, where positive outcomes can be established to enhance patient care.

References:

• How hypothesis tests work: Confidence intervals and confidence levels. (2019, June 25). Statistics By Jim. https://statisticsbyjim.com/hypothesis-testing/hypothesis-tests-confidence-intervals-levels/
• LibGuides: Maths: Hypothesis testing. (2020, May 13). LibGuides at La Trobe University. https://latrobe.libguides.com/maths/hypothesis-testing More about hypothesis testing. (n.d.). https://bolt.mph.ufl.edu/6050-6052/unit-4/module-12/more-about-hypothesis-testing

A Sample Answer 6 For the Assignment: HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Title:  HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Hypothesis testing and confidence intervals are significant contributors to evidence-based practice and guide the protocols of patient intervention, ultimately influencing patient outcomes. Hypothesis testing can be employed to correlate associations between variables, which leads to quality improvement and supports improvements to standard practice. However, it is the role of nursing leadership to responsibly interpret research and apply it for the improved patient outcomes (Ambrose, 2021). Confidence intervals represent the risk that the research could be wrong. The combined use of hypothesis testing with confidence intervals allows for results to be extrapolated and applied to the general population. While the confidence interval can be important in applying the research results from the sample to the population, it is important to be aware of the ability to manipulate the confidence interval if thorough methods are not employed. Small sample size and lack of replication are both contributing factors to misinterpreted p-values or statistical significance.

In the example of a 2019 study by Henson et al., the authors compared select outcomes associated with postoperative analgesic approaches in patients undergoing TKA. The two approaches were femoral nerve blocks and periarticular injections. Using 2-tailed t tests, the researchers examined pain perception, use of opioid analgesics, length of stay and total cost of care. Additionally, they examined readmission rates using a 2-sample z test for proportions. With a stratified population sample of 144 patients, the authors found an association between patients who received an FNB with a lower pain perception (p = .0497). Results also showed a possible correlation between a decrease in opioid consumption in those who received a PAI (p = .037). By calculating the mean, along with the standard deviation of the five selected variables, the authors were able to gain data regarding reported decrease in pain, it is clear that the study needs further replication with additional factors examined (Henson, et al., 2019). However, studies like this set the groundwork for future studies to build upon and gain even more in-depth knowledge regarding the topic or practice at hand.

References

Ambrose, J. (2021). Clinical inquiry and hypothesis testing. In Grand Canyon University (Ed.). Applied statistics for health care (ch.3). https://bibliu.com/app/#/view/books/1000000000581/epub/Chapter3.html#page_31

Henson, K. S., Thomley, J. E., Lowrie, L. J., & Walker, D. (2019). Comparison of Selected Outcomes Associated with Two Postoperative Analgesic Approaches in Patients Undergoing Total Knee Arthroplasty. AANA Journal, 87(1), 51–57.