Final answer:
Non-parametric statistical tests are used when assumptions about the data are not met. They are often used for ordinal, nominal, or skewed data, and do not rely on specific distribution assumptions.
Step-by-step explanation:
Non-parametric statistical tests are used when certain assumptions about the data, such as normality and equal variances, are not met. These tests do not rely on specific distribution assumptions and are more flexible in their application. They are often used when analyzing ordinal or nominal data, or when the data is heavily skewed or has outliers.
Some common examples of non-parametric tests include the Mann-Whitney U test, Wilcoxon signed-rank test, Kruskal-Wallis test, and chi-square test for independence.
Non-parametric tests can provide reliable results even with small sample sizes or non-normally distributed data, making them a valuable tool in statistical analysis.