MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups

Student Name

Capella University

MHA-FPX 5017 Data Analysis for Health Care Decisions

Prof. Name

Date

Hypothesis Testing for Group Differences

In inferential statistics, populations of individuals are subject to analysis and testing using hypothesis testing, which aids in comparing datasets and facilitating conclusive decision-making. This process revolves around two types of hypotheses: null and alternative. The null hypothesis posits no significant difference in data compared side by side, while the alternative hypothesis suggests substantial differences within the dataset (Hacker & Hatemi-J, 2022).

Research Scenario

The comparison of productivity levels between two clinics, Clinic 1 and Clinic 2, is conducted using null and alternative hypotheses. The null hypothesis (H0) proposes no difference in productivity between the two clinics, while the alternative hypothesis (Ha) supports differences in productivity. These hypotheses can be expressed as equations: [H_0: \text{Clinic 1} = \text{Clinic 2}] [H_a: \text{Clinic 1} \neq \text{Clinic 2}].

Selection of Statistical Tests

The determination of a normal distribution between the samples guides the choice of tests. A symmetric distribution ensures symmetrical data presentation, while the current asymmetric appearance signifies unequal variances, favoring the Wilcoxon Signed-Rank test (Chang & Perron, 2017).

Two-Sample t-tests Results

Both samples possess a sufficient sample size (n = 100) warranting an independent t-test for estimating the normal distribution. Presented below are two independent t-tests, one assuming equal variances and the other assuming unequal variances.

Table 1: Two-Sample t-test Assuming Equal Variances

Table 2: Two-Sample t-test Assuming Unequal Variances

Interpretation and Recommendation

Clinic 2 exhibits a higher mean than Clinic 1 in both scenarios, indicating better performance. With p-values less than the significance level (α = 0.05), the null hypothesis is rejected. Consequently, Clinic 1’s patient visit ratios differ from Clinic 2’s based on the data.

MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups

Recommendation

According to the data, Clinic 2 appears to outperform Clinic 1, albeit with a relatively close performance. Remedial actions for underperforming clinics involve analyzing clinical workflows, scheduling and booking software, staff education, billing, and coding practices. A comprehensive analysis identifies deficient areas, enabling administrators to formulate data-driven recommendations for enhancing clinic performance (Aspalter, 2023).

References

Aspalter, C. (2023). Evaluating and Measuring Exactly the Distances between Aggregate Health Performances: A Global Health Data and Welfare Regime Analysis. Social Development Issues, 45(1), 1-36. Link

Chang, S. Y., & Perron, P. (2017). Fractional Unit Root Tests Allowing for a Structural Change in Trend under Both the Null and Alternative Hypotheses. Econometrics, 5(1), 5. DOI

MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups

Hacker, R. S., & Hatemi-J, A. (2022). Model selection in time series analysis: using information criteria as an alternative to hypothesis testing. Journal of Economic Studies, 49(6), 1055-1075. DOI