Non-parametric methods are statistical techniques that do not rely on specific assumptions about the underlying population distribution. These methods are often used when the data does not meet the assumptions required by parametric methods. In Paper I, there are several statistical concepts related to non-parametric methods that are important to understand.

Mann-Whitney U test:
The Mann-Whitney U test, also known as the Wilcoxon rank-sum test, is a non-parametric test used to compare two independent samples. It is used when the data is ordinal or continuous but not normally distributed. The test compares the ranks of the observations between the two groups to determine if there is a significant difference.

Wilcoxon signed-rank test:
The Wilcoxon signed-rank test is a non-parametric test used to compare two related samples or matched pairs. It is used when the data is ordinal or continuous but not normally distributed. The test ranks the absolute differences between paired observations and determines if there is a significant difference.

Kruskal-Wallis test:
The Kruskal-Wallis test is a non-parametric test used to compare three or more independent samples. It is used when the data is ordinal or continuous but not normally distributed. The test ranks the observations within each group and determines if there is a significant difference in the medians among the groups.

Friedman test:
The Friedman test is a non-parametric test used to compare three or more related samples or matched pairs. It is used when the data is ordinal or continuous but not normally distributed. The test ranks the observations within each group and determines if there is a significant difference in the medians among the groups.

Sign test:
The sign test is a non-parametric test used to compare two related samples or matched pairs. It is used when the data is nominal or ordinal and not normally distributed. The test compares the number of positive and negative differences between paired observations to determine if there is a significant difference.

Spearman's rank correlation coefficient:
Spearman's rank correlation coefficient is a non-parametric measure of the strength and direction of the monotonic relationship between two variables. It is used when the data is ordinal or continuous but not normally distributed. The coefficient ranges from -1 to 1, where -1 indicates a perfect negative monotonic relationship and 1 indicates a perfect positive monotonic relationship.

Overall, non-parametric methods in Paper I provide alternative statistical techniques for analyzing data that does not meet the assumptions of parametric methods. These methods are particularly useful when dealing with ordinal or non-normally distributed data, providing researchers with valuable tools to draw meaningful conclusions.