Explain your analysis choices in plain language
Examiners will ask why you used specific tests and what the numbers mean. You don't need to recite formulas — you need to explain why each test was chosen and what it tells you.
What it tests: Are the paired score differences normally distributed?
Your result: Non-normal (reject null of normality).
Why it matters: If differences are non-normal, parametric tests (paired t-test) may be less reliable. This is why Wilcoxon became your primary test.
How to say it: "We first checked whether the paired score differences were normally distributed using the Shapiro-Wilk test. Because the test indicated non-normality, we used the Wilcoxon signed-rank test as the primary analysis."
What it tests: Is there a statistically significant difference between two related groups (pre vs post), without assuming normal distribution?
How it works: It looks at the ranks of the differences, not the raw values. This makes it robust to non-normal distributions and outliers.
Your result: Significant (p < 0.05).
How to say it: "The Wilcoxon signed-rank test showed a significant within-group increase from pre-test to post-test, indicating a positive score direction after the Kit-Build activity."
Key distinction: This tests whether there is a difference, not how big the difference is. For that, you need effect size.
What it tests: Same question as Wilcoxon, but assumes normal distribution of differences.
Your role for it: Reference/robustness check only. Report it alongside Wilcoxon to show the result is consistent across methods.
How to say it: "A paired t-test was also conducted as a reference comparison and confirmed the same direction of result."
What it measures: The standardized magnitude of the mean difference between two paired measurements.
Interpretation:
Why "reference": Because your primary test is non-parametric (Wilcoxon), Cohen's d (a parametric effect size) is reported as a reference, not the primary effect size estimate.
How to say it: "Cohen's d was reported as a reference effect size for the mean paired difference. The effect size [was small/medium/large], consistent with the score direction observed."
What it measures: Like Cohen's d but corrected for small sample bias. More accurate for N < 20.
Where you used it: For the median-split comparison (low vs high pre-test groups), which had smaller subgroups.
How to say it: "Hedges' g was used instead of Cohen's d because the subgroups were small. The effect size was large."
Spearman rho: Measures rank correlation. Does not assume linear relationship or normal distribution. Used for Kit-Build score vs score change.
Pearson r: Measures linear correlation. Assumes normal distribution and linear relationship. Used for pre-test score vs gain.
Your result: Spearman showed no significant relationship between Kit-Build score and score change. Pearson showed a significant negative correlation between pre-test score and gain.
How to say it: "We used Spearman's rho because we did not assume a linear relationship between Kit-Build reconstruction score and comprehension score change. The correlation was not significant."
This matters because examiners might ask: "Why did some students decline?" The honest answer: they were already near the ceiling, so any small random variation looks like a decline. The median-split analysis confirms this: low pre-test group gained, high pre-test group declined slightly.
If an examiner asks about a statistical detail you've forgotten:
This is always better than guessing. Examiners respect honesty about what you know vs. what you'd need to verify.
1. Why was Wilcoxon chosen as the primary test instead of the paired t-test?
2. A student asks: "Why did students with high pre-test scores decline?" What's your answer?
3. What's the difference between "statistically significant" and "large effect size"?
Read Field (2018), Chapter 6 (Correlations) and Chapter 8 (Comparing two means) — specifically sections on choosing between parametric and non-parametric tests.
Ask me anything. If you want me to re-explain Shapiro-Wilk, effect sizes, or any specific test in simpler terms, just say so.