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

Hypothesis Testing for Differences Between Groups Populations of individuals undergo analysis and testing utilizing hypothesis testing within inferential statistics, aiding in comparing datasets and facilitating conclusive decision-making. Two types of hypotheses, null and alternative, frame research questions, with one positing truth. The null hypothesis proposes no significant difference in data compared side by side, while the alternative hypothesis suggests substantial differences within the dataset (Hacker & Hatemi-J, 2022). The directive entails comparing the productivity levels of clinics one and two using the methods of null and alternative hypotheses. In this context, the null hypothesis (H0) suggests no difference in productivity between the two clinics, while the alternative hypothesis (Ha) supports differences in productivity. Expressed as equations: [H_0: \text{Clinic 1} = \text{Clinic 2}] [H_a: \text{Clinic 1} \neq \text{Clinic 2}] MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups 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). 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 Clinic 1 Clinic 2 Mean 124.32 145.03 Variance 2188.543 1582.514 Observations 100 100 Pooled Variance 1885.529 – Hypothesized Mean Difference 0 – df 198 – t Stat -3.37247 – P(T<=t) one-tail 0.000448 – t Critical one-tail 1.65258 – P(T<=t) two-tail 0.000896 – t Critical two-tail 1.972017 – Table 2: Two-Sample t-test Assuming Unequal Variances Clinic 1 Clinic 2 Mean 124.32 145.03 Variance 2188.543 1582.514 Observations 100 100 Pooled Variance 1885.529 – Hypothesized Mean Difference 0 – df 193 – t Stat -3.37247 – P(T<=t) one-tail 0.00045 – t Critical one-tail 1.652787 – P(T<=t) two-tail 0.0009 – t Critical two-tail 1.972332 – 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. 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. http://library.capella.edu/login?qurl=https%3A%2F %2Fwww.proquest.com%2Fscholarly-journals%2Fevaluating-measuring-exactlydistances-