What is the difference between parametric and non-parametric statistical tests?

Parametric statistical tests assume that the data being analyzed follows a specific distribution, usually the normal distribution. These tests make assumptions about the population parameters, such as the mean and variance. Non-parametric tests, on the other hand, do not make any assumptions about the population parameters and are used when the data does not follow a specific distribution. Non-parametric tests are considered to be more flexible and robust, but may have less power compared to parametric tests under certain conditions. Ultimately, the choice between parametric and non-parametric tests depends on the nature of the data and the research question being studied.

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