Non-parametric statistics is a branch of statistics that does not assume the underlying data follows a particular probability distribution. Unlike traditional parametric statistics, non-parametric statistics are not limited by pre-defined distributions and allow for greater flexibility when analyzing data.
Non-parametric statistics are used in situations where there is no clear probability distribution that can be assumed, such as when the data is categorical or when the sample size is too small to accurately model a normal distribution. Non-parametric tests are also used when the data is not normally distributed due to outliers or other factors.
Non-parametric methods are often used in areas such as survey research, to compare the responses of different groups, or to measure the strength of relationships between variables. Non-parametric tests can also be used to assess differences in proportions or to measure the probability of a given event occurring.
Non-parametric statistics are an important part of data analysis and can be used to uncover valuable insights even when the data does not follow a particular probability distribution.
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