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Unlike parametric statistics, which rely on specific assumptions about the distribution of the data, nonparametric methods provide flexible and distribution-free alternatives for data analysis. Nonparametric methods often utilize resampling techniques, such as bootstrapping and permutation tests, which involve repeatedly sampling from the data to estimate the sampling distribution and calculate p-values. These techniques are particularly useful when parametric assumptions cannot be met. Nonparametric statistics provides valuable tools for data analysis when assumptions about the population distribution are uncertain or cannot be made. These methods offer flexibility, robustness and distribution-free inference, making them applicable in a wide range of research areas and practical applications.

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