HLT 362 Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research
Grand Canyon University HLT 362 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 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 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 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 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 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.
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How to Write the Body for HLT 362 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 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 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 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 Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research
Re: Topic 3 DQ 2
Hypothesis testing and confidence intervals are two important statistical tools that are commonly used together in health care research. “The hypothesis is a prediction of what will happen between the two variables” (June Helbig & Jayme Ambrose, 2022). “The confidence interval (CI) is an interval estimate for the mean” (June Helbig & Jayme Ambrose, 2022). “It is a range of values that are set close to the mean either in a positive or negative direction” (June Helbig & Jayme Ambrose, 2022). “For the null to be rejected, 95% of the values need to set close to the mean” (June Helbig & Jayme Ambrose, 2022). While hypothesis testing helps determine if an observed effect is statistically significant, confidence intervals provide a range of values in which the true population parameter likely falls. A study might test the hypothesis that a new rehabilitation technique reduces recovery time after surgery. Upon rejecting the null hypothesis, the study might report a 95% CI for the average reduction in recovery days. In a hospital setting, a CI between 3 to 5 days confirms technique effectiveness and expected outcomes. This dual approach offers both statistical and practical significance, guiding clinicians in setting realistic expectations and improving patient care.
References:
June Helbig & Jayme Ambrose. (2022). Applied Statistics for Health Care. Grand Canyon University (Ed.). (2022). Applied statistics for health care (2nd ed.).
Sample Answer 2 for HLT 362 Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research
The confidence interval (CI) is an interval estimate for the mean and is a range of values that are set close to the mean in a negative or positive direction. For the null to be rejected, 95% of the values must be set close to the mean. The range of these values determines the effect. There is no certainty that either of these possibilities are true. The CI reflects the risk of the researcher possibly being wrong. The basis of the rejection or failure to reject the null hypothesis is based on the CI at 95%. A CI of 95% says that 95% of research projects like the one completed will involve the true mean, but 5% will not, which means that there are five chances in 100 of being wrong. If one reduces the confidence interval it will increase the risk of error. The CI is calculated by knowing the sample size, looking at the mean, the standard deviation and by choosing the level of confidence interval from the table. The calculation will give what your values will fall between in order to show 95% confidence (Helbig and Ambrose, 2022).
Healthcare providers using evidence-based medicine to inform practice must use clinical judgment to figure out the practical importance of studies from careful evaluation of the designs, sample size, likelihood of type I and type II errors, power, data analysis and reporting of statistical findings such as p values and 95% CI or both (Shreffler and Huecker, 2020).
In a hypothesis test there are two different hypotheses. Each one is attempted to be validated. Confidence intervals then will give a range of values that are more likely with a certain level of confidence. An example of this is testing new fall risk measures that have been implemented. The effect of the measures must be hypothesized, find the parameters on how well the measures are working on the patient population and then try to figure out the parameters on the success of the interventions.
References:
Shreffler, Jacob, and Martin R. Huecker. “Hypothesis Testing, P Values, Confidence Intervals, and Significance.” PubMed, StatPearls Publishing, 2020, www.ncbi.nlm.nih.gov/books/NBK557421/.
June Helbig & Jayme Ambrose. (2022). Applied Statistics for Health Care. Grand Canyon University (Ed.). (2022). Applied statistics for health care (2nd ed.).
Sample Answer 3 for HLT 362 Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research
The hypothesis is the question the researcher wants to answer, the clinical inquiry in healthcare, the research design, how the data is gathered and analyzed is determined by the question or hypothesis. In healthcare we aim to find correlations and answers within the data to provide for better patient population outcomes. Correlation does not prove causation. Clinical significance determines whether the research has a practical application to an individual or a group. It also is used to determine health care decisions made by leadership. 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).
The confidence interval helps to reject the null hypothesis. The confidence interval is an interval estimate for the mean. It is a range of values that are set close to the mean either in a positive or negative direction. For the null to be rejected, 95% of the values need to be set close to the mean. The range of values determines the effect.
A CI informs the investigator and the reader about the power of the study and whether or not the data are compatible with a clinically significant treatment effect. Confidence intervals also provide a more appropriate means of analysis for studies that seek to describe or explain, rather than to make decisions about treatment efficacy. The logic of hypothesis testing uses a decision-making mode of thinking which is more suitable to randomized controlled trials (RCTs) of health care interventions. Hypothesis testing to determine statistical significance was initially intended to be used only in randomized experiments such as RCTs which are typically not feasible in clinical research involving identification of risk factors, etiology, clinical diagnosis, or prognosis. The use of CIs allows for hypothesis testing and it allows a more flexible approach to analysis that accounts for the objectives of each investigation (Savage, 2003).
Ambrose, J. (2018). Retrieved from https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3
Savage, S. (2003). Advantages of confidence intervals in clinical research. Retrieved from: https://www.redorbit.com/news/science/18686/advantages_of_confidence_intervals_in_clinical_research/
Sample Answer 4 for HLT 362 Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research
The hypothesis testing alone is not sufficient to offer a complete picture of what is being tested. Instead, confidence intervals (CI) complement the hypothesis-testing process by providing a range of values within which the true population parameter falls. The range of values provided by the confidence intervals includes the accurate value of statistical constraint within the population targeted. Researchers often use 95% CI. An investigator can set any level between 90%CL to 99% CL. The CL of 95% shows that suppose the study is undertaken 100%, the range would have an actual value of 95. Hence, CL offers more evidence concerning the precision of an estimate in comparison to the p-value (Shreffler & Huecker, 2023).
These are critical facts to know when using the CL and hypothesis testing:
- A researcher has a bad CI if the null hypothesized value is present in the CI because it will lead to a high p-value.
- The null hypothesized value will be at a point of no difference or zero value if the CI is zero, predicting a chance of finding no difference.
- If the null hypothesized value falls within the CI, the p-value will be greater than 5%.
Suppose the null hypothesized value falls outside the presented C; the p-value will be less than 5%. Therefore, both CI and hypothesis intervals can be used to support conclusions (Shreffler & Huecker, 2023).
Examples of how the hypothesis and confidence intervals are utilized together in healthcare research include:
Comparative studies where health researchers are comparing two groups like treatment. The hypothesis testing assists in determining whether there are significant differences between the two groups, while CI estimates the magnitude of the difference. For example, hypothesis tests may confirm the superiority of one technique over the other in a clinical trial comparing two surgical techniques. At the same time, confidence intervals will specify the range of anticipated improvement. Another example is predictive modeling, where the hypothesis testing validates the significance of the predictor variable while CI quantifies their predictive accuracy. For example, in a model predicting heart attack, the hypothesis confirms the significance of variable factors like cholesterol levels, while CI shows the precision of the predictors (Hespanhol et al., 2019).
An example in the workplace is when testing the effectiveness of a drug on a patient’s cholesterol level. The hypothesis is supported when the p-value is less than the predetermined significant level of 0.05. In this example, the confidence interval could indicate that the new drug reduces collateral levels by 5 to mm Hg. Such an interval quantifies the uncertainty associated with the presented estimate.
Therefore, both the hypothesis and confidence intervals are inferential techniques. They utilize a sample to test the strength and validity of the hypothesis of the population parameter. The only difference is the point of reference, whereby the hypothesis tests focus on the null hypothesized parameter while CI focuses on the estimate of the sample parameters.
References
Hespanhol, L., Vallio, C. S., Costa, L. M., & Saragiotto, B. T. (2019). Understanding and interpreting confidence and credible intervals around effect estimates. Brazilian journal of physical therapy, 23(4), 290-301. https://www.sciencedirect.com/science/article/pii/S141335551831058X
Shreffler, J., & Huecker, M. R. (2023). Hypothesis testing, P values, confidence intervals, and significance. In StatPearls [Internet]. StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK557421/