Data-Driven Decision Making

Data-Driven Decision Making

Week 4: Data-Driven Decision Making for Health care Administration


Confidence Intervals and Hypothesis Tests


You have likely encountered confidence intervals and hypothesis tests in your previous statistics courses. How are confidence intervals and hypothesis tests related to healthcare administration practice?


As a healthcare administration leader, you will likely want to test whether certain improvement initiatives are working more effectively than others. Additionally, you might want to test whether a certain improvement initiative is worth investing in given positive health outcomes and cost of resources. Hypothesis tests are valuable tools that may assist the healthcare administration leader in executing such decision making.


1. This week, you apply confidence intervals and hypothesis tests for healthcare administration problems. You also reflect on the types of information healthcare administration leaders might derive from hypothesis testing.


Learning Objectives

Students will:


· Evaluate confidence intervals

· Execute hypothesis testing with confidence intervals

Photo Credit: Laureate Education




Learning Resources

Note: To access this week’s required library resources, please click on the link to the Course Readings List, found in the Course Materials section of your Syllabus.


Required Readings


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”

Fulton, L. V., Ivanitskaya, L. V., Bastian, N. D., Erofeev, D. A., & Mendez, F. A. (2013). Frequent deadlines: Evaluating the effect of learner control on healthcare executives’ performance in online learning. Learning and Instruction, 23, 24–32.


Required Media


The Doctoral Journey. (2013). SPSS tutorial: One-way ANOVA. Retrieved from


The Doctoral Journey. (2013). SPSS tutorial: Independent t test. Retrieved from


The Doctoral Journey. (2013). SPSS tutorial: Paired sample t test. Retrieved from




Discussion Part (2 pages)



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.


1. 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.


2. For this Discussion, review the resources for this week, and consider how a CI might be used to support hypothesis testing in a healthcare scenario.


By Day 3

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.


By Day 5

Continue the Discussion and respond to your colleagues in one or more of the following ways:


· Ask a probing question, substantiated with additional background information, evidence, or research.

· Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives.

· Offer and support an alternative perspective, using readings from the classroom or from your own research in the Walden Library.

· Validate an idea with your own experience and additional research.

· Make a suggestion based on additional evidence drawn from readings or after synthesizing multiple postings.

· Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence.


Submission and Grading Information

Grading Criteria

To access your rubric:


Week 4 Discussion Rubric


Post by Day 3 and Respond by Day 5

To participate in this Discussion: Week 4 Discussion




Assignment Part(5 pages) APA format 7th ed.





Confidence Intervals and Hypothesis Testing in Medical Scenarios


Confidence intervals (CIs) and hypothesis tests assist healthcare administration leaders in executing decision making for a wide variety of conditions or experiences in a health services organization. Interpreting the significance of the information provided in hypothesis testing can ensure that healthcare administration leaders execute the best and appropriate measures possible to ensure effective healthcare delivery.


1. For this Assignment, review the resources for this week. Then, review your course text, and complete Problem 66 on page 410 and Case Study 9.3 on pages 412-413. Consider the type of analysis tools you may use to best fit the case study provided.


The Case Study: Removing VIOXX from The Market


For years, the drug VIOXX, developed and marketed by Merck, was one of the blockbuster drugs on the market. One of a number of so-called Cox-2 anti-inflammatory drugs, Vioxx was considered by many people a miracle drug for alleviating the pain from arthritis and other painful afflictions. VIOXX was marketed heavily on television, prescribed by most physicians, and used by an estimated two million Americans. All of that changes in October 2004, when the results of a large study were released. The study, which followed approximately 2600 subjects over a period of about 18 months, concluded that VIOXX use over a long period of time caused a significant increase in the risk of developing serious heart problems. Merck almost immediately pulled Vioxx from the American market and doctors stopped prescribing it. On the basis of the study, Merck faced not only public embarrassment but the prospect of huge financial losses.

More specifically, the study had 1287 patients use Vioxx for an 18-months period, and it had another 1299 patients use a placebo over the period. After 18 months, 45 of the Vioxx patients had developed serious heart problems, whereas only 25 patients on the placebo developed such problems.

Given these results, would you agree with the conclusion that Vioxx caused a significant increase in the risk of developing serious heart problems? Given these results, would you agree with the conclusion that Vioxx caused a significant increase in risk of developing serious heart problem?

First, answer this from a purely statistical point of view, where significant means statistically significant. What hypothesis should you test, and how should you run the test? When you run the test, what is the corresponding p-value?

Next, look at it from the point of view of patients. If you were a Vioxx user, would these results cause you significant worry?

After all, some of the subjects who took placebos also developed heart problems, and 45 might not be considered that much larger than 25. Finally, look at it from Merck’s point of view. Are the results practically significant to the company? What does it stand to lose? Develop an estimate, no matter how wild it might be, of the financial losses Merck might incur. Just think of all those American VIOXX users and what they do.



For Chapter 9, problem 66, you will need to download the file P09_66.xlsx from the textbook companion website Under “Book Resources”, click on “Student Downloads” to view the downloadable files. Click “Problem Files” and download the zipped file Open the zipped file, and select folder “Problem Files” and then select folder “Chapter 09” to access the file P09_66.xlsx.


Page 410 on Textbook Problem 66

A company is concerned with the high cholesterol levels of many of its employees. T help combat the problems, it opens an exercise facility and encourages its employees to use this facility. After a year, it chooses a random 100 employees who claim they use the facility regularly, and another 200 who claim they don’t use it at all. The cholesterol levels of these 300 employees are checked, with the results shown in the file P09_66.xlsx.

(a) Is this sample evidence “proof” that the exercise facility, when used, tends to lower the mean cholesterol level? Phrase this as a hypothesis-testing problem and do the appropriate analysis. Do you feel comfortable that your analysis answers the question definitively (one way or the other)? Why or why not?

(b) Repeat part a, but replace “mean cholesterol level” with “percentage with level over 215” (The company believes that any level over 215 is dangerous.)

(c) What can you say about causality? Could you ever conclude from such a study that the exercise causes low cholesterol? Why or why not?


The Assignment: (5 pages)


· Complete Problem 66 on page 410 of your course text using SPSS.

· Complete Case Study 9.3, “Removing Vioxx From the Market,” on pages 412-413 of your course text.

· This case study requires only calculations and analysis of them, so you may complete this case study using any tool you choose.


By Day 7

Submit your answers and embedded analysis (SPSS and other analysis) as a Microsoft Word management report.


Submission and Grading Information

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Grading Criteria

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Week 4 Assignment Rubric


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Submit Your Assignment by Day 7

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