Non-parametric methods are statistical techniques that do not assume a specific distribution for the data, allowing for greater flexibility when dealing with real-world datasets that may not fit common distributions. They are particularly useful for analyzing ordinal data or data with unknown distributions, making them robust tools in exploratory data analysis and hypothesis testing.