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.
Answer:
Data in health care is jus as important as the care and safety we provide to our patients. Understanding the clinical aspect of statistics can be confusing for some. In order to see what has worked and what has not, we can look at studies and the process that lead to the results. When looking at studies and the accuracy of the results; understanding confidence intervals is key. A confidence interval (CI) provides information obtained from a sample. This information most likely will have the unknown value of the population parameter (Frost, n.d., para. 1). It also gives an imprecise measurement of the sample. Because this information might not give a true and accurate measurement of the whole population; it give the best information for size of the sample population (Patino & Ferreira, 2015, para. 3).
Confidence intervals are important to understand ans see the unpredictability of the sample. This sample can give us a picture of effect size of the source population (Patino & Ferreira, 2015, para. 4). One of the clinical benefits of using sample data is the convenience, lower cost and shorter time to obtain data. When using this small sample we can make educated decisions on how a bigger group (from where the sample is taken from).
In one study conducted to understand the relationship between asthma patients and the risk of developing obstructive sleep apnea in adults. Participants were randomly recruited and followed for four years. After that time those with asthma were compared with those without; it demonstrated they were at higher risk of developing obstructive sleep apnea (Relative risk [RR] = 1.39, 95% CI: 1.06-1.82, p = 0.03) (Patino & Ferreira, 2015, para. 1).
References
Frost, J. (n.d.). How hypothesis work: confidence intervals and confidence levels. Retrieved from https://statisticsbyjim.com/hypothesis-testing/hypothesis-tests-confidence-intervals-levels/
Patino, C. M., & Ferreira, J. C. (2015, November-December 2015). Confidence intervals: a useful statistical tool to estimate effect sizes in the real world. Journal Brasileiro de Pneumologia, 41(4), 565-566. http://dx.doi.org/10.1590/S1806-37562015000000314