Mann-Whitney U Test
Mann-Whitney U Test is one of the statistical analyses applied automatically in MerQur. This page illustrates — on a Social, Humanities & Admin Sciences sample dataset — how the analysis is run, what the MerQur output looks like, and how the result is reported in APA 7 format.
🎯 What is it for?
The Mann-Whitney U Test automatically performs all required assumption checks (normality, homogeneity of variance, etc.) for the relevant data type in the background and presents the results with a clear table + chart. Automatic interpretation in the APA 7 standard, effect sizes such as Cohen’s d/η²/R², and 95% confidence intervals are reported.
📌 When is it used?
- Statistical analysis of measurements in the Social, Humanities & Admin Sciences domain
- To produce APA 7-compatible result tables for academic publications
- Hypothesis testing and decision-making processes
- Undergraduate / master’s / PhD theses after the appropriate method has been selected
📐 Assumptions
- Appropriate scale — Variables must be at the measurement level required by the analysis (nominal/ordinal/interval/ratio)
- Independent observations — Observations must come from individuals independent of one another
- Sufficient sample size — The minimum n requirement for the analysis must be met
- Outlier check — Outliers must be detected and evaluated
If assumptions are violated, MerQur automatically suggests a non-parametric or robust alternative.
🛠 How to do it in MerQur
Load the data. Select the sample file from File → Open. MerQur auto-detects column types.
Select the analysis. From the left side select Mann-Whitney U Test.
Panel assignments (form fields in the program):
- Group Column:
konum - Value Column:
yasam_doyumu
Optional settings. Effect size ✓ · 95% confidence interval ✓ · Assumption checks (automatic).
▶ Run — click the button. Results are produced automatically as a table + chart.
📄 Export to Word. APA 7-formatted report with italic statistical symbols.
📊 Sample Dataset — Social, Humanities & Administrative Sciences
ℹ Note: The scenario, MerQur output and interpretation below were produced by actually running the real example dataset in MerQur. Numeric results on your own data will differ; the goal is to show how the analysis is set up and interpreted end-to-end.
🎬 Example File
This analysis is demonstrated on the following example dataset for Social, Humanities & Administrative Sciences:
Sosyal_Beseri/14_mann_whitney_agriculture_quality.xlsx
🎬 Scenario
We compare two independent groups on an ordinal measure. Since normality fails, Mann-Whitney is appropriate.
⚙️ Variable Selection
- Grouping (categorical): location
- Test variable: survival_satisfaction
- >> ADVANCED PARAMETERS (optional in the form — what they do):
- Hypothesis direction; continuity correction.
- Computation method: auto / exact (precise for small n) / asymptotic.
- Effect sizes: CLES and Z/sqrt(N) (rank-biserial r already shown).
- Descriptives (median etc.).
Data Preview (First 5 Rows)
Preview unavailable: [Errno 2] No such file or directory: ‘F:/MERQUR_CODE/merqur/english/datasets/Sosyal_Beseri/14_mann_whitney_agriculture_quality.xlsx’
📈 MerQur Output
💬 Interpretation
We compared the distribution of two independent groups (location) on an ordinal/skewed measure (survival_satisfaction) based on ranks rather than means: U = 1121.5, p = 0.049, small-medium effect (r = -0.25). The difference is borderline significant. Mann-Whitney is the nonparametric counterpart of the t-test; when normality fails or the scale is ordinal, it is the right way to compare two groups. In education/social research it is a robust choice for ordinal measures like satisfaction/grade. >> ADVANCED PARAMETERS (optional in the form — what they do): – Hypothesis direction; continuity correction. – Computation method: auto / exact (precise for small n) / asymptotic. – Effect sizes: CLES and Z/sqrt(N) (rank-biserial r already shown). – Descriptives (median etc.).
⚠ Common Mistakes
- Misidentifying the data type (e.g., loading a categorical variable as numeric)
- Skipping assumption checks and going straight to the p-value
- Failing to report effect size — APA 7 requires both p and effect size
- Failing to apply a Type I error correction (Bonferroni/Tukey) in multiple comparisons
- Not switching to a non-parametric alternative when n is insufficient
📹 Video Walkthrough
Watch the video below for an end-to-end walkthrough of this analysis on a Social, Humanities & Admin Sciences file.
▶ Mann-Whitney U Test — video walkthrough
A complete end-to-end walkthrough of this analysis in MerQur, narrated on screen. Narration is in Turkish.
🎓 Education Sciences · 🧪 Natural Sciences & Mathematics · 🏛 Architecture, Planning & Design · ⚙ Engineering · 🏥 Health Sciences · 🏃 Sport Sciences · 🌾 Agriculture, Forestry & Aquatic
📚 If You Used This Analysis, Cite MerQur
If you performed this analysis using MerQur in a scientific study, please use the citation below as part of your academic citation obligations (APA 7):
Örücü, Ö. K. (2026). MerQur: Integrated Academic Data Analysis & Reporting Platform [Computer software] (Version 1.0.0). https://doi.org/10.53463/merqur.2026001
For BibTeX, RIS, EndNote and the English citation form: all citation formats →
- American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
- Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.