Healthcare administration leaders are asked to makeevidence-based decisions on a daily basis. Sometimes, thesedecisions involve high levels of uncertainty, as you have examinedpreviously. Other times, there are data upon which evidence-basedanalysis might be conducted. This week, you will be asked to thinkof scenarios where building and interpreting confidence intervals(CIs) would be useful for healthcare administration leaders toconduct a two-sided hypothesis test using fictitious data. Forexample, Ralph is a healthcare administration leader who isinterested in evaluating whether the mean patient satisfactionscores for his hospital are significantly different from 87 at the.05 level. He gathers a sample of 100 observations and finds thatthe sample mean is 83 and the standard deviation is 5. Using at-distribution, he generates a two-sided confidence interval (CI)of 83 +/- 1.984217 *5/sqrt(100). The 95% CI is then (82.007,83.992). If repeated intervals were conducted identically, 95%should contain the population mean. The two-sided hypothesis testcan be formulated and tested just with this interval. Ho: Mu = 87,Ha: Mu<>87. Alpha = .05. If he assumes normality and thatpopulation standard deviation is unknown, he selects thet-distribution. After constructing a 95% CI, he notes that 87 isnot in the interval, so he can reject the null hypothesis that themean satisfaction rates are 87. In fact, he has an evidence-basedanalysis to suggest that the mean satisfaction rates are not equalto (less than) 87.
Consider how a CI might be used to support hypothesis testing ina healthcare scenario. Post a description of a healthcare scenariowhere a CI might be used, and then complete a fictitious two-sidedhypothesis test using a CI and fictitious data.
