DNP-BIO 7-2

Similarity Report

PAPER NAME

BIO 7-ASSIGNMENT2.docx AUTHOR

-

WORD COUNT

636 Words CHARACTER COUNT

3444 Characters

PAGE COUNT

5 Pages FILE SIZE

12.7KB

SUBMISSION DATE

Nov 18, 2025 5:33 PM EST REPORT DATE

Nov 18, 2025 5:34 PM EST

26% Overall Similarity The combined total of all matches, including overlapping sources, for each database.

19% Internet database 11% Publications database

Crossref database Crossref Posted Content database

25% Submitted Works database

Excluded from Similarity Report

Manually excluded sources

Summary

1

Student’s name: Maria Henriquez Cueto

Instructor: Lourdes Valverde

Institution: Ana G. Mendez University

Course: Biostatistics

Date: November 18, 2025

Parametric vs. Nonparametric Decision

6

2

The Analysis and Justification

In the present case, we intend to compare two independent groups (WBC Group 0, WBC

Group 1) on a continuous outcome. In normal conditions, the standard parametric test of means

is the independent samples t-test (Vrbin, 2022).

Shapiro-Wilk test results

 Group 0: W = 0.394, p < .001

 Group 1: W = 0.800, p < .001

The two groups are found to be significantly different with large p-values, and this

implies a great violation of normality. Moreover, the standard deviations do not coincide

significantly (SD = 9.384 vs. 4.405), which is an indication of heterogeneity of variance.

Together with the visual cue, indicating that the data most likely look skewed or not normally

distributed, these diagnostics demonstrate that the conditions to assume a parametric t-test are

not fulfilled.

The Mann-Whitney U test is the best for this case. The reason why the Mann-Whitney U

test was the right and appropriate test of this analysis is that the dependent variable is non-

normally distributed, and group variances are not equal. This nonparametric test is not based on

the assumption of normality and compares distributions according to the rank, which gives it a

greater strength of analysis in cases where data breaks other parametric assumptions (Chicco et

al., 2025).

Mann-Whitney U test results

U = 2339.000, p = 0.750

The p-value shows that therno significant difference was found in the two groups.

1

3

3

5

5

7

3

Importance of p-value important in This Context.

The p-value is used to indicate whether the differences between the groups were as a

result of chance or not. The nonparametric result presented as well as the parametric result

indicate that the p-value is about 0.75 which is substantially greater than the conventional a =.05

threshold.

This means:

 No evidence to disprove the null hypothesis.

 The outcome on which WBC Group 0 and Group 1 do not differ

significantly.

Notably, a p-value that is nonsignificant is not a sign that the groups are the same; it only

shows substandard evidence of difference within the sample. Also, a t-test would not be reliable

in terms of p-value, which would further justify the necessity to use the p-value of the Mann-

Whitney U test instead, due to a breach of the normality assumption (Midway & White, 2025).

In nonparametric tests, the p-value is calculated with an identical inferential purpose,

except that the difference between means is not used, rather a rank-based calculation is

performed. In this respect, the statistical conclusion is the most optimal based on the Mann-

Whitney U p-value (Midway & White, 2025).

Conclusion

The appropriate test of analysis is the Mann-Whitney U test since the two independent

groups have been capitulated in regard to normality and unequal variance. The test result of the

statistically significant difference between WBC Group 0 and Group 1 is not significant (p

3

8

4

=.750). The p-value plays a significant role in any interpretation that is carried out to establish

whether the difference that is realized is significant or it could have occurred as a chance. The p-

value in this study is high and there are adequate reasons to conclude that there is no sufficient

evidence to declare that a real difference exists between groups.

References

1

5

Chicco, D., Sichenze, A., & Jurman, G. (2025). A simple guide to the use of Student’s t-test,

Mann-Whitney U test, Chi-squared test, and Kruskal-Wallis test in biostatistics. BioData

Mining, 18(1). https://doi.org/10.1186/s13040-025-00465-6

Midway, S., & White, J. W. (2025). Testing for normality in regression models: mistakes abound

(but may not matter). Royal Society Open Science, 12(4).

https://doi.org/10.1098/rsos.241904

Vrbin, C. M. (2022). Parametric or Nonparametric Statistical Tests: Considerations when

Choosing the Most Appropriate Option for your Data. Cytopathology, 33(6), 663–667.

https://doi.org/10.1111/cyt.13174

2

4

Similarity Report

26% Overall Similarity Top sources found in the following databases:

19% Internet database 11% Publications database

Crossref database Crossref Posted Content database

25% Submitted Works database

TOP SOURCES The sources with the highest number of matches within the submission. Overlapping sources will not be displayed.

1 American College of Education on 2025-09-07 8% Submitted works

2 mro.massey.ac.nz 5% Internet

3 biodatamining.biomedcentral.com 3% Internet

4 Kean, Yin. "Bridging the Gap: Examining Behavioral Determinants in AI ... 3% Publication

5 University of Chichester on 2025-03-21 3% Submitted works

6 Ana G. Méndez University on 2025-11-17 2% Submitted works

7 University of Northumbria at Newcastle on 2025-11-11 1% Submitted works

8 BB9.1 PROD on 2025-10-09 1% Submitted works

Sources overview

Similarity Report

Excluded from Similarity Report

Manually excluded sources

EXCLUDED OVERLAPPING SOURCES

Submitted to Ana G. Méndez University on 2025-11-10 Submitted works

4%

Submitted to Ana G. Méndez University on 2025-11-17 Submitted works

4%

Submitted to Ana G. Méndez University on 2025-11-05 Submitted works

3%

Submitted to Ana G. Méndez University on 2025-11-18 Submitted works

3%

Submitted to Ana G. Méndez University on 2025-11-17 Submitted works

3%

Excluded from Similarity Report