Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research HLT 362
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Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research HLT 362
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: Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research HLT 362
Title: Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research HLT 362
REPLY TO DISCUSSION
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Hello class,
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 conf
ident we can be when drawing conclusions about the parameter of our sample population.
An example of this is testing the overall performance of a new medication being offered at a clinic. One must hypothesis 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.
Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research HLT 362 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
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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).
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.
Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research HLT 362 References
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
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Irene,
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:
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
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Hello Irene,
great post. I just want to add that the use of hypothesis testing and confidence intervals can be seen in quality improvement projects throughout an organization. In healthcare, we aim to find correlations and answers to our questions (hypothesis) within the data to provide better patient outcomes. Through these projects, we ask the question, find, plan and implement processes or the evidence, and evaluate the outcomes by building a concept or framework for the investigation.
Ambrose, J. (2018). What are statistics and why are they important to health science. In Applied statistics for health care (1 ed.). Grand Canyon University: Grand Canyon University
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Hello Irene,
I hope you’re having a wonderful weekend also I really love your discussion post and I found it every interesting. Your research was great and really went in depth into what each term is and how they work in conjunction with one another good job and I can’t wait to see how you do your discussion post next week.
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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. While there is not 100% certainty that either of these possibilities could be true, the CI reflects the risk of the researcher being wrong. It is important that the statistical analysis of the data and its associated probability are true. The basis of rejection or failure to reject the null hypothesis is based on the CI of 95%. A CI of 95% says that 95% of research projects like the one completed will include the true mean, but 5% will not, meaning that there are five chances in 100 of being wrong. Reducing the confidence interval increases the risk for error (Ambrose, 2018).
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).
The use of hypothesis testing and confidence intervals can be seen in quality improvement projects throughout an organization. In healthcare, we aim to find correlations and answers to our questions (hypothesis) within the data to provide better patient outcomes. Through these projects, we ask the question, find, plan and implement processes or the evidence, and evaluate the outcomes by building a concept or framework for the investigation. In my place of work the rate of readmission can be projected through the use of hypothesis testing by inputting those precautionary factors that can help in the reduction of patients coming back to hospital after they have been discharge. While at the same time, the confidence interval is use to determine average rehospitalization within any particular month, and this help to improve the quality of service provided by the organization
Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research HLT 362 Reference:
Ambrose, J. (2018). What are statistics and why are they important to health science. In Applied statistics for health care (1 ed.). Grand Canyon University: Grand Canyon University.
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/
- DM
Destiny Montes
replied toShirley Brown
Sep 4, 2022, 1:24 AM(edited)
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Hello Shirley,
I hope you’re having a wonderful weekend also I really love your discussion post and I like how you explain the question and the importance of both the hypothesis along with the confidence I really help me understand both and why they are important to the healthcare community thank you Shirley and I’m sure that you will get A on the DQ.
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LB
Loreta Binion
replied toShirley Brown
Sep 9, 2022, 2:28 AM
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Hello Shirley,
If the null hypothesized value is found in our confidence interval, then that would mean we have a bad confidence interval and our p-value would be high. Typically our null hypothesized value will be 0 (point of no difference), and if we find 0 in our confidence interval then that would mean we have a good chance of actually finding no difference, which is typically the opposite of what we want.
In other words, if the null hypothesized value falls within the confidence interval, then the p-value is always going to be larger than 5%. Conversely, if the null hypothesized value falls outside of our confidence interval then the p-value is going to be less than 5%.
Grand Canyon University. (Ed.). (2018). Applied statistics for health care. https://www.gcumedia.com/digital-resources/grand-canyonuniversity/2018/applied-statistics-for-health-care_1e.php
AM
Abosede Morenikeji
Sep 2, 2022, 2:47 PM
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Confidence intervals and hypothesis tests are similar in that they are both inferential methods that rely on an approximated sampling distribution. Confidence intervals use data from a sample to estimate a population parameter. Hypothesis tests use data from a sample to test a specified hypothesis. Hypothesis testing requires that we have a hypothesized parameter (Penn State Eberly College of Science, 2022).
Hypothesis testing and confidence intervals are used together in health care research in other to be able to determine the level of significance by understanding and knowing the p value and not just relying on the researchers (Shreffler & Huecker, 2022).
Confidence intervals are defined as “the range of values around the statistic within a population parameter, at a specific parameter While hypothesis testing is used to determine solely a positive correlation, or determine if there is a positive or negative correlation.
While these two are different in correlation they are trying to find, they are very similar and can be
utilized together in order to obtain accurate results (Ambrose, 2018).
An example of how hypothesis testing and confidence intervals are used together in health care research can be when a nurse try to research a new medication for hypertension on some of their hypertensive patients, the nurse take like 10 sample of hypertensive patients and give them the new medication, and also, 10 hypertensive patient not taking the new medication to see if the medication is effective on the hypertensive patients.
Ambrose, J. (2018). Applied statistics for health care: Clinical inquiry and hypothesis testing. Retrieved from https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3
Penn State Eberly College of Sciene, (2022). Elementary Statistics. Confidence Intervals & Hypothesis Testing. Retrieved from https://online.stat.psu.edu/stat200/lesson/6/6.6
Shreffler J, & Huecker M. R. (2022). Hypothesis testing, p values, confidence intervals, and significance. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK557421/#_NBK557421_pubdet_
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Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research HLT 362 Grading Rubric
| Performance Category | 100% or highest level of performance
100% 16 points |
Very good or high level of performance
88% 14 points |
Acceptable level of performance
81% 13 points |
Inadequate demonstration of expectations
68% 11 points |
Deficient level of performance
56% 9 points
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Failing level
of performance 55% or less 0 points |
| Total Points Possible= 50 | 16 Points | 14 Points | 13 Points | 11 Points | 9 Points | 0 Points |
| Scholarliness
Demonstrates achievement of scholarly inquiry for professional and academic topics. |
Presentation of information was exceptional and included all of the following elements:
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Presentation of information was good, but was superficial in places and included all of the following elements:
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Presentation of information was minimally demonstrated in all of the following elements:
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Presentation of information is unsatisfactory in one of the following elements:
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Presentation of information is unsatisfactory in two of the following elements:
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Presentation of information is unsatisfactory in three or more of the following elements
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| 16 Points | 14 Points | 13 Points | 11 Points | 9 Points | 0 Points | |
| Application of Course Knowledge
Demonstrate the ability to analyze and apply principles, knowledge and information learned in the outside readings and relate them to real-life professional situations |
Presentation of information was exceptional and included all of the following elements:
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Presentation of information was good, but was superficial in places and included all of the following elements:
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Presentation of information was minimally demonstrated in the all of the following elements:
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Presentation of information is unsatisfactory in one of the following elements:
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Presentation of information is unsatisfactory in two of the following elements:
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Presentation of information is unsatisfactory in three of the following elements
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| 10 Points | 9 Points | 6 Points | 0 Points | |||
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Demonstrated all of the following:
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Demonstrated 3 of the following:
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Demonstrated 2 of the following:
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Demonstrated 1 or less of the following:
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The following was present:
AND
AND
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The following was present:
AND/OR
AND/OR
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The following was present:
AND/OR
AND/OR
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The following was present:
AND/OR
AND/OR
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The following was present:
AND/OR
AND/OR
AND/OR
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The following was present:
AND/OR
AND/OR
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| 0 Points Deducted | 5 Points Lost | |||||
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Requirements |
Demonstrated the following:
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Failed to demonstrate the following:
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| 0 Points Lost | 5 Points Lost | |||||
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