MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups
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MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups

MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups

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Capella university

MHA-FPX 5017 Data Analysis for Health Care Decisions

Prof. Name

Date

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-between%2Fdocview%2F2867617355%2Fse-2%3Faccountid%3D27965

MHA FPX 5017 Assessment 2 Hypothesis Testing for Differences Between Groups

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. https://doi.org/10.3390/econometrics5010005

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. https://doi.org/10.1108/JES-09-2020-0469