Advantages and disadvantages of non-parametric methods

It is fair to say that statisticians tend to disagree somewhat regarding the value of non-parametric methods. Some statisticians view them very favourably while others are reluctant to use them unless there is no other alternative. 

Clearly the main advantage of a non-parametric method is that it makes essentially no assumptions about the underlying distribution of the data. In contrast, the corresponding parametric method makes specific assumptions, for example, that the data are normally distributed. Does this matter Well, as mentioned earlier, the t-tests, even though in a strict sense they assume normality, are quite robust against departures from normality. In other words you have to be some way off normality for the p-values and associated confidence intervals to be become invalid, especially with the kinds of moderate to large sample sizes that we see in our trials. Most of the time in clinical studies, we are within those boundaries, particularly when we are also able to transform data to conform more closely to normality. 

Further, there are a number of disadvantages of non-parametric methods  

For these reasons non-parametric methods are used infrequently within the context of clinical trials and they tend only to be considered if it is clear that a corresponding parametric approach, either directly or following a data transformation, is unsuitable. 

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