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Here are two examples of where non-parametric statistics would be used:

• Comparing the median incomes of two different groups of people. The median income is a non-parametric measure of central tendency, so a non-parametric test such as the Mann-Whitney U test would be used to compare the median incomes of the two groups.
• Comparing the satisfaction levels of customers with two different products. Customer satisfaction can be measured on an ordinal scale, such as a Likert scale. To compare the satisfaction levels of customers with the two products, a non-parametric test such as the Wilcoxon rank-sum test could be used.

Other examples of where non-parametric statistics might be used include:

• Analyzing data from surveys and questionnaires.
• Analyzing data from small sample sizes.
• Analyzing data that is not normally distributed.
• Analyzing data that contains outliers.

Non-parametric statistics are a valuable tool for researchers who are working with data that does not meet the assumptions of parametric statistics.

Here are some additional advantages of using non-parametric statistics:

• They are less sensitive to outliers than parametric tests.
• They can be used with smaller sample sizes.
• They are easier to interpret than some parametric tests.

However, it is important to note that non-parametric tests are generally less powerful than parametric tests, meaning that they are less likely to detect a statistically significant difference between two groups when a difference actually exists.