When are non-parametric statistical methods most useful?
- When the data does not meet the assumptions for parametric methods
- When the data follows a normal distribution
- When the data is free from outliers
- When there is a large amount of data
Non-parametric statistical methods are most useful when the data does not meet the assumptions for parametric methods. For example, if the data does not follow a normal distribution, or if there are concerns about outliers or skewness, non-parametric methods may be appropriate.
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