Mann-Whitney U Test Assignment Help — Non-Parametric SPSS Alternative to the T-Test

What Is the Mann-Whitney U Test and When Do You Use It?

The Mann-Whitney U test is the non-parametric alternative to the independent samples t-test. It compares two independent groups on an outcome, but instead of comparing means it compares the rank distributions of the two groups. Use it when your outcome is ordinal, or when it’s continuous but clearly violates the t-test’s normality assumption (especially with small samples where that violation can’t be assumed away).

If your data reasonably meets the independent t-test’s assumptions, use that test instead. It has more statistical power when its assumptions hold.

Assumptions You Must Check Before Running It in SPSS

Independence of observations between and within groups.
The dependent variable should be at least ordinal.
Unlike the t-test, Mann-Whitney does not require normality: that’s the entire reason to use it.
For a clean interpretation of “which group scores higher,” the two groups’ distributions should have a similar shape (not necessarily normal, just similarly shaped). If shapes differ substantially, the test still runs but the interpretation shifts from “medians differ” to “distributions differ.”

How to Run It in SPSS (Step by Step)

Go to Analyze > Nonparametric Tests > Legacy Dialogs > 2 Independent Samples.
Move your outcome variable into Test Variable List.
Move your two-group categorical variable into Grouping Variable, then click Define Groups and enter the two group codes.
Ensure Mann-Whitney U is checked under Test Type, then click OK.

How to Interpret the Output

In the Test Statistics table, read the Mann-Whitney U value and its associated Asymp. Sig. (2-tailed), your p-value.
Report medians and interquartile ranges (IQR) per group instead of means and SDs: these are the appropriate descriptive statistics for a rank-based test.
Calculate effect size as r = Z ÷ √N, using the Z-value SPSS reports in the same table (0.1 small, 0.3 medium, 0.5 large).

How to Report the Results in APA Format

A Mann-Whitney U test showed that satisfaction scores were significantly higher in the treatment group (Mdn = 8) than the control group (Mdn = 6), U = 312.50, Z = −2.87, p = .004, r = .32.

Mann-Whitney U vs Independent Samples T-Test: Understanding the Difference

Both compare two independent groups, but the independent t-test compares means and assumes normally distributed data; the Mann-Whitney U test compares rank-based distributions and makes no normality assumption. Running a t-test on badly non-normal data (especially with a small sample) risks an unreliable result. Mann-Whitney is the safer choice in that situation, at some cost in statistical power if the data actually was normal enough for the t-test.

If Mann-Whitney doesn’t quite fit your design, one of these related non-parametric tests likely will:

Kruskal-Wallis H Test: the same rank-based logic extended to three or more independent groups
Wilcoxon Signed-Rank Test: for two related (not independent) measurements
McNemar’s Test: for paired binary or categorical data instead of a continuous outcome
Spearman’s Rank Correlation: for testing a relationship between two ordinal or non-normal variables, rather than a group difference

Not sure which one your data needs? See the full SPSS statistical test guide, or get help with this specific assignment.