nonparametriset

nonparametric methods are statistical approaches that do not assume a specific probability distribution for the population being analyzed. Unlike parametric methods, which rely on assumptions about the underlying distribution such as normality, nonparametric techniques are more flexible and make fewer distributional assumptions. These methods are particularly useful when dealing with ordinal data, small sample sizes, or when the distribution of the data is unknown or non-normal. common nonparametric tests include the mann-whitney u test, kruskal-wallis test, wilcoxon signed-rank test, and the chi-square test. these tests often use ranks or categorical information rather than actual values. nonparametric methods are valuable in various fields including psychology, medicine, social sciences, and environmental studies where data may not meet parametric assumptions. while they offer robustness and flexibility, they may have less statistical power than parametric methods when the assumptions of parametric tests are met. the choice between parametric and nonparametric methods should be based on the nature of the data, sample size, and research objectives.