HLT 362 Topic 3 Discussion Question Two
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.
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.
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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
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/