MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups MHA-FPX 5017 Data Analysis for Health Care Decisions Hypothesis Testing for Differences Between Groups

Populations of individuals are analyzed and tested using hypothesis testing within inferential statistics, which aids in comparing datasets and facilitating informed decision-making. Two types of hypotheses, null and alternative, frame research questions, with one asserting a truth. The null hypothesis posits that there is no significant difference in the data when compared side by side, while the alternative hypothesis suggests that substantial differences exist within the dataset (Hacker & Hatemi-J, 2022).

The objective is to compare the productivity levels of clinics one and two using the null and alternative hypotheses. In this context, the null hypothesis (H0) states that there is no difference in productivity between the two clinics, while the alternative hypothesis (Ha) indicates that differences in productivity do exist. This can be expressed mathematically as: [H_0: \text{Clinic 1} = \text{Clinic 2}] [H_a: \text{Clinic 1} \neq \text{Clinic 2}].

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

Both samples have a sufficient size (n = 100), which justifies the use of an independent t-test for estimating the normal distribution. Below are the results of 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

Clinic 2 demonstrates a higher mean than Clinic 1 in both scenarios, indicating superior performance. With p-values below the significance level (α = 0.05), the null hypothesis is rejected. Therefore, the patient visit ratios for Clinic 1 differ from those of Clinic 2 based on the data.

Recommendation

The data suggests that Clinic 2 outperforms Clinic 1, although the performance gap is relatively narrow. To address the underperformance of clinics, it is essential to analyze clinical workflows, scheduling and booking software, staff training, and billing and coding practices. A comprehensive analysis will identify areas needing improvement, enabling administrators to develop data-driven recommendations to enhance 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.