It is possible that your data does not look Gaussian or fails a normality test, but can be transformed to make it fit a Gaussian distribution. Even if your data does not have a Gaussian distribution. This gives some incentive to use them if possible. If your data has a Gaussian distribution, the parametric methods are powerful and well understood. A large portion of the field of statistics is concerned with methods that assume a Gaussian distribution: the familiar bell curve.
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