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When using hypothesis testing in research, the researcher needs to consider the clinical inquiry in healthcare, how the data will be gathered and analyzed and the design of the research. The hypothesis or question determines how this will be done. In healthcare, we are aiming to find a correlation and answer in the data to better provide for our patient population outcomes. It is vital to know that correlation does not prove causation; clinical significance determines the practical application to a group or an individual. Clinical significance is also utilized in the application for improving the quality of life of the individual and provides a path from health research to actual patient care (Grand Canyon University, 2018).
The confidence interval (CI) is used to estimate the mean, in either a positive or negative direction determining the effect. For example, you might implement protocols for performing intubation on patients in the hospital setting. To evaluate whether these protocols were successful in improving intubation rates, you could measure the intubation rate over time in one group randomly assigned to training in the new protocols, and compare this to the intubation rate over time in another control group that did not receive training in the new protocols. If the exact p-value is reported the relationship between confidence intervals and hypothesis testing is close. “However, the objective of the two methods is different: hypothesis testing relates to a single conclusion of statistical significance vs. no statistical significance, confidence intervals provide a range of plausible values for your population” (National EMSC Data Analysis Resource Center, & the University of Utah, 2016).
Confidence intervals provide the ability to make better decisions for the patient and can are evident in quality improvement projects through an. Through improvement projects nurses can ask questions, implement processes based on evidence and evaluate the outcomes. For example, once vitals are collected at set the data collected is processed through the computer which generates alerts for the nurse to notify them of significant changes for a patient. Allowing the nurse to maintain and implement safety measures as needed.
Grand Canyon University (Ed). (2018). Applied statistics for health care. Retrieved from
National EMSC Data Analysis Resource Center, & the University of Utah. (2016). Confidence Intervals. Retrieved from