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.
REPLY TO DISCUSSION
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.
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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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
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.
replied toIrene Igbinosa
Sep 3, 2022, 2:36 AM
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.
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