Business & Finance Assignment 2 hypothesis

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Hypothesis Testing for Differences Between Groups

Melissa Croft

Data Analysis for Health Care Decisions

Capella University

September 2022

Introduction

Healthcare leaders are faced with making difficult decisions daily and rely on data to help guide them to the soundest choice. Our investor is faced with the decision of acquiring one of two clinics based on which clinic has been most productive over the last 100 months. A hypothesis must be determined, and data analysis performed to determine the statistical differences between the two clinics.

Statistical Analysis

clinic1	clinic2
Mean	124.32	Mean	145.03
Standard Error	4.68	Standard Error	3.98
Median	134.5	Median	149.5
Mode	150	Mode	175
Standard Deviation	46.78	Standard Deviation	39.78
Sample Variance	2188.54	Sample Variance	1582.51
Kurtosis	-0.44	Kurtosis	0.26
Skewness	-0.51	Skewness	-0.06
Range	183	Range	221
Minimum	24	Minimum	42
Maximum	207	Maximum	263
Sum	12432	Sum	14503
Count	100	Count	100
Largest(1)	207	Largest(1)	263
Smallest(1)	24	Smallest(1)	42

Hypothesis Testing Between Two Different Groups

Descriptive statistics analysis and hypothesis testing will demonstrate a statistical difference between the clinic's productivity. Hypothesis testing is a statistical means of testing an assumption. There are two main types of hypothesis testing, the classical approach, and the probability value or p-value method (Frey, 2018). The p-value depicts the probability of either rejecting or accepting the hypothesis proposed. The null (H0) hypothesis is defined as no difference between the clinics. The equation for the null is H0:clinic 1 clinic 2

The alternative hypothesis (HA) is usually what the investigator wants to prove as true (Elliott, n.d.). In this case HA :clinic 1 = clinic 2. The mean for clinic 1 is 124.32, with a median of 134.5 and a mode of 150. The standard deviation was calculated as 46.78 patient visits with kurtosis of -0.44. The clinic saw a minimum of 24 patients and a maximum of 207. The mean for clinic 2 is 145.03, with a median of 149.5 and a mode of 175. The standard deviation was calculated as 39.78 with kurtosis of 0.26. The number of patients in the clinic range from a minimum of 42 to 263. A p-value, or probability value, is a number describing how likely it is that the null hypothesis is true (McLeod, 2019). A two-sample t-test was used to analyze the difference between the two clinics. The data in the t-test can be used to accept or reject the null hypothesis. It was shown that the p-value of 0.003 was less than the alpha of 0.05. A p-value less than 0.05 indicates evidence that the null hypothesis is false, and therefore reject the null and accept the alternative hypothesis (McLeod, 2019). Based on the data analysis, clinic two has proven to be more productive than clinic one.

Narrative Summary

Significance testing is designed to provide a reliable means for making black-and-white decisions through accepting or rejecting null hypotheses (Wilkinson, 2014). As an investor, it is imperative to consider other variables as to why the null is true. Barriers to accessing ambulatory care may be a pivotal contributor to productivity variation between the two proposed clinics. Access to appointments is essential for patient care, referring physicians, downstream revenue, and practice efficiency (Matteson-Kome et al., 2014). A patient will turn to another facility or physician if they cannot be seen promptly, leading to lower clinic utilization with a long wait time.

In addition, the investor must consider the market competition and pricing structure. Over the last several years, in light of the global pandemic, there has been a movement to make healthcare costs more transparent. The clinic's competitive position compared to its competitors significantly influences pricing policy (Cleverley, 2017). Patients are increasingly seeking cheaper and more convenient healthcare options. Another environmental factor to consider is the staffing levels between clinic one and clinic two. . In 2022, nearly 1.7 million people have quit their healthcare jobs (Gordon, 2022). Without providers and clinical and administrative staff, a clinic cannot function to its total capacity and see optimized patient throughput. Reducing staff shortages can increase productivity and improve patient outcomes while minimizing inefficiencies within the clinical workflow (Apaydin et al., 2022).

Conclusion

Investing in a new venture always carries risk, however data driven decisions allow for more informed decision making. Using hypothesis testing we are able to prove that clinic 2 would be a better investment. However, it is important that investors understand and apply methods of statistical inference correctly to prevent mistakes in the interpretation of results, and subsequently to bad research decisions (Wilkinson, 2014).

References

Apaydin, E. A., Anderson, J. A., Rahman, B., & Parr, N. J. (2022, February).  Home - books - NCBI. National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/books

Cleverley, W. O. (2017). Essentials of Health Care Finance (8th Edition). Jones & Bartlett Learning. https://capella.vitalsource.com/books/9781284142808

Frey, B. B. (Ed.). (2018).  Descriptive statistics.   The SAGE encyclopedia of educational research, measurement, and evaluation (Vols. 1–4). Sage.

Gordon, D. (2022, May 25). Amid Healthcare's great resignation, burned out workers are pursuing flexibility and passion. Forbes. https://www.forbes.com/sites/debgordon/2022/05/17/amid-healthcares-great-resignation-burned-out-workers-are-pursuing-flexibility-and-passion/?sh=15212e067fda

Matteson-Kome, M. L., Lopez, K. T., Sliger, A. D., Mathews, M. J., & Bechtold, M. L. (2014). Improving care access for new patients in an outpatient gastroenterology clinic: A novel approach. Missouri medicine. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6173548/

McLeod, S. (1970, January 1). What a P-value tells you about statistical significance https://www.simplypsychology.org/p-value.html

Wilkinson, M. (2014). Distinguishing between statistical significance and Practical/Clinical meaningfulness using statistical inference.  Sports Medicine, 44(3), 295-301. http://library.capella.edu/login?qurl=https%3A%2F%2Fwww.proquest.com%2Fscholarly-journals%2Fdistinguishing-between-statistical-significance%2Fdocview%2F1623362835%2Fse-2