Confidence intervals in healthcare administration

Discussion: Confidence Intervals in Healthcare Administration

Healthcare administration leaders are asked to make  evidence-based decisions on a daily basis. Sometimes, these decisions  involve high levels of uncertainty, as you have examined previously.  Other times, there are data upon which evidence-based analysis might be  conducted.

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This week, you will be asked to think of scenarios where building and  interpreting confidence intervals (CIs) would be useful for healthcare  administration leaders to conduct a two-sided hypothesis test using  fictitious data.

For example, Ralph is a healthcare administration leader who is  interested in evaluating whether the mean patient satisfaction scores  for his hospital are significantly different from 87 at the .05 level.  He gathers a sample of 100 observations and finds that the sample mean  is 83 and the standard deviation is 5. Using a t-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 test can be formulated and tested just with this  interval. Ho: Mu = 87, Ha: Mu<>87. Alpha = .05. If he assumes  normality and that population standard deviation is unknown, he selects  the t-distribution. After constructing a 95% CI, he notes that 87 is not  in the interval, so he can reject the null hypothesis that the mean  satisfaction rates are 87. In fact, he has an evidence-based analysis to  suggest that the mean satisfaction rates are not equal to (less than)  87.

By Day 3

Post a description of a healthcare scenario where a  CI might be used, and then complete a fictitious two-sided hypothesis  test using a CI and fictitious data.


Albright,  S. C., & Winston, W. L. (2017). Business analytics: Data analysis and  decision making (6th ed.). Stamford, CT: Cengage Learning.
Chapter 8, “Confidence Interval Estimation”
Chapter 9, “Hypothesis Testing”

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